September 2012 Lower Tropospheric Temperature Anomaly Analysis From The University of Alabama At Huntsville

Phillip Gentry has provided us with the September 2012 lower tropospheric temperature anomaly analysis from the University of Alabama at Huntsville. It is presented below [click each image for a clearer view]. Note the large spatial variations in the temperature anomalies.

Global Temperature Report: August 2012

Changing satellites as instruments die

Global climate trend since Nov. 16, 1978: +0.14 C per decade

September temperatures (preliminary)

Global composite temp.: +0.34 C (about 0.61 degrees Fahrenheit) above 30-year average for September.

Northern Hemisphere: +0.35 C (about 0.63 degrees Fahrenheit) above 30-year average for September.

Southern Hemisphere: +0.33 C (about 0.59 degrees Fahrenheit) above 30-year average for September.

Tropics: +0.15 C (about 0.22 degrees Fahrenheit) above 30-year average for September.

August temperatures (revised):

Global Composite: +0.21 C above 30-year average

Northern Hemisphere: +0.21 C above 30-year average

Southern Hemisphere: +0.20 C above 30-year average

Tropics: +0.06 C above 30-year average

(All temperature anomalies are based on a 30-year average (1981-2010) for the month reported.)

Notes on data released Oct. 8, 2012:

September 2012 was the third warmest September in the 34-year satellite temperature record, according to Dr. John Christy, a professor of atmospheric science and director of the Earth System Science Center at The University of Alabama in Huntsville. Three of the last four Septembers were warmer than September 1998, during the El Niño Pacific Ocean warming event “of the century.” The last September that was cooler than the 30-year baseline seasonal norm was in 2000.

Compared to seasonal norms, the coldest spot on the globe in September was (again) at the South Pole, where the Antarctic spring temperature averaged 3.31 C (almost 6 degrees Fahrenheit) colder than normal. The “warmest” spot was just north of Monbetsu, Japan, where temperatures in September averaged 3.72 C (about 6.7 degrees Fahrenheit) warmer than seasonal norms.

The temperatures reported in this report are from different instruments than have been used in the recent past, Christy said.

“Some things are just out of our control,” he said. “In the past three years our backbone satellite – NASA’s AQUA, which has been operating since 2002 – has experienced an increase in ‘noise.’ Until now, however, the differences between temperature values recorded by AQUA and two other satellites, NOAA 15 and NOAA 18, were within 0.1 C. That is within our typical margin of error for monthly global values and not of much concern.

“In September, the difference jumped to 0.2 C. Looking at the daily values, that gap was increasing as the month ended. It appears that for our climate project, AQUA is no longer useful.”

AQUA has on-board propulsion that allows it to maintain a stable orbit, which means the temperature data it collected was also stable. Orbital drift (east or west) and orbital decay cause systemic changes in temperature data, either warmer or cooler depending on which way the satellite’s orbit is shifting. While the UAHuntsville team has developed and published techniques for correcting errors caused by orbital drift or decay, data from a satellite in a stable orbit is easier to process and should be more reliable.

There is, however, no technique to correct for a failing instrument.

“We haven’t used NOAA-15 or NOAA-18 in the past few years because they each are drifting in orbit,” Christy said. “NOAA-15 is moving to slightly warmer temperature and NOAA-18 to slightly cooler. It is clear, however, that the slight differences between the temperature values they report (less than 0.1 C) are small and their average will be very close to the actual temperatures, as their errors will cancel each other out.

“We have implemented a simple solution for the data problem, which we will call version 5.5 of the UAHuntsville satellite dataset,” Christy said. “For the data beginning in January 2010 we will use the average of NOAA-15 and NOAA-18, and will leave out AQUA. The only change is the source of data. As it turns out, the long-term global climate trend doesn’t change, because the real problem only developed in the past month.”

The UAHuntsville team is working now on version 6.0 of the dataset, which will more precisely account for issues like the small orbital drifts in NOAA-15 and NOAA-18. There is no schedule for the release of the new dataset: “We are taking our time and having an independent scientist write the new code from scratch, to insure that it is testable and transportable. That takes time. Until the new version is released, the values provided by version 5.5 will give us more accurate information than relying on the instrument on the AQUA satellite.”

Archived color maps of local temperature anomalies are available on-line at:

http://nsstc.uah.edu/climate/

The processed temperature data is available on-line at:

vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt

As part of an ongoing joint project between UAHuntsville, NOAA and NASA, John Christy, a professor of atmospheric science and director of the Earth System Science Center (ESSC) at The University of Alabama in Huntsville, and Dr. Roy Spencer, an ESSC principal scientist, use data gathered by advanced microwave sounding units on NOAA and NASA satellites to get accurate temperature readings for almost all regions of the Earth. This includes remote desert, ocean and rain forest areas where reliable climate data are not otherwise available.

The satellite-based instruments measure the temperature of the atmosphere from the surface up to an altitude of about eight kilometers above sea level. Once the monthly temperature data is collected and processed, it is placed in a “public” computer file for immediate access by atmospheric scientists in the U.S. and abroad.

Neither Christy nor Spencer receives any research support or funding from oil, coal or industrial companies or organizations, or from any private or special interest groups. All of their climate research funding comes from federal and state grants or contracts.

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New Paper “Simulating Regime Structures In Weather And Climate Prediction Models” By Dawson Et Al 2012

Ryan Maue has alerted us to a new paper

Dawson A., T. N. Palmer and S. Corti: 2012: Simulating Regime Structures in Weather and Climate Prediction Models. Geophyscial Research Letters. doi:10.1029/2012GL053284 In press.

This paper further documents the gross inadequacies of the CMIP5 model runs as I have summarized, for example, in my posts

The Hindcast Skill Of The CMIP Ensembles For The Surface Air Temperature Trend” By Sakaguchi Et Al 2012

More CMIP5 Regional Model Shortcomings

CMIP5 Climate Model Runs – A Scientifically Flawed Approach

See also my post on a 2008 paper by Tim Palmer

Comments On The Article By Palmer et al. 2008 “Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts”

and my BAMS comment on his paper (and a related one by Jim Hurrell)

Pielke Sr., R.A., 2010: Comments on “A Unified Modeling Approach to Climate System Prediction”. Bull. Amer. Meteor. Soc., 91, 1699–1701, DOI:10.1175/2010BAMS2975.1,

The abstract of the Dawson et al 2012 GRL paper reads [highlight added]

It is shown that a global atmospheric model with horizontal resolution typical of that used in operational numerical weather prediction is able to simulate non-gaussian probability distributions associated with the climatology of quasi-persistent Euro-Atlantic weather regimes. The spatial patterns of these simulated regimes are remarkably accurate. By contrast, the same model, integrated at a resolution more typical of current climate models, shows no statistically significant evidence of such non-gaussian regime structures, and the spatial structure of the corresponding clusters are not accurate. Hence, whilst studies typically show incremental improvements in first and second moments of climatological distributions of the large-scale flow with increasing model resolution, here a real step change in the higher-order moments is found. It is argued that these results have profound implications for the ability of high resolution limited-area models, forced by low resolution global models, to simulate reliably, regional climate change signals.

Examples of key excerpts read

This paper presents a study of the ability of a state-of-the-art global atmospheric model, integrated in atmosphere-only mode at two different horizontal resolutions representative of NWP and climate models, to simulate Euro-Atlantic regime structures as found in reanalysis datasets. It is shown that whilst the NWP resolution model simulates the regimes well, the same model integrated at climate resolution has no statistically significant regimes at all.

This study supports the growing recognition that there is no more complex problem in computational science than that of simulating climate, and next generation climate simulators should be developed at current NWP resolutions – the need for Earth System complexity and ensemble capability notwithstanding.

We have shown that a low resolution atmospheric model, with horizontal resolution typical of CMIP5 models, is not capable of simulating the statistically significant regimes seen in reanalysis, yet a higher resolution configuration of the same model simulates regimes realistically. This result suggests that current projections of regional climate change may be questionable. This finding is also highly relevant to regional climate modelling studies where lower resolution global atmospheric models are often used as the driving model for high resolution regional models. If these lower resolution driving models do not have enough resolution to realistically simulate regimes, then then boundary conditions provided to the regional climate model could be systematically erroneous. It is therefore likely that the embedded regional model may represent an unrealistic realization of regional climate and variability.

This is a very convincing refutation of the claims in the Mears et al 2012 paper

Linda O. Mearns, Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.

that I posted on in

Comment Submitted To BAMS On The Mearns Et Al 2012 Paper

Follow Up On My E-Mail Request To Linda Mearns Of NCAR

E-Mail To Linda Mearns On The 2012 BAMS Article On Dynamic Downscaling

“The North American Regional Climate Change Assessment Program: Overview of Phase I Results” By Mearns Et Al 2012 – An Excellent Study But It Overstates Its Significance In The Multi-Decadal Prediction Of Climate

As a final comment, the Dawson et al 2012 also confirms what I wrote in a paper in 1991

Pielke, R.A., 1991: Overlooked scientific issues in assessing hypothesized  greenhouse gas warming. Environ. Software, 6, 100-107.

It is the recommendation, as I wrote in that paper with the title

INABILITY FOR GCM MODELS TO PROPERLY RESOLVE THE EVOLUTION OF EXTRATROPICAL AND TROPICAL CYCLONES DUE TO THEIR POOR SPATIAL RESOLUTION

with the text

The horizontal grid spacing of general circulation models is around 400 km. As shown by Pielke (1984), as least four grid increments are required to reasonably represent an atmospheric feature, thus this grid resolution would only permit features 1600 km or larger to be reasonably represented in the models, Since extratropical cyclones often are observed to have horizontal wavelengths as small as 500 km or so, they are poorly represented in these models, Since these features provide the major physical mechanism for the exchange of heat, moisture, and momenlum between the subtropics and the polar regions, the inability of GCM representations to adequately represent these exchanges is a serious shortcoming. Tropical cyclones, which also provide an important mechanism for exchanges between the tropics and higher latitude is even more poorly represented since its scales of important physical processes includes the eye wall which can be tens of kilometers in radial size. Pielke (1988) discusses this shortcoming further.

Pielke, R.A., 1984: Mesoscale meteorological modeling. Academic Press, New York, N.Y., 612 pp.

Pielke, R.A., 1988: Evaluation of climate change using numerical models. In “Monitoring Climate for the Effects of Increasing Greenhouse Gas Concentrations. Proceedings of a Workshop”. R.A.
Pielke and T. Kittel, Eds., Cooperative Institute for Research in lhe Almosphere (CIRA),

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“Understanding The Impact Of Dam-Triggered Land Use/Land Cover Change On The Modification Of Extreme Precipitation” By Woldemichael Et Al 2012

We have a new paper accepted (in press) on the role of landscape processes on climate. It is

Woldemichael, A. T., F. Hossain, R. Pielke Sr., and A. Beltrán-Przekurat (2012), Understanding the impact of dam-triggered land use/land cover change on the modification of extreme precipitation, Water Resour. Res., 48, WXXXXX, doi:10.1029/2011WR011684.

The abstract reads [highlight added]

Two specific questions are addressed in this study regarding dams (artificial reservoirs). (1) Can a dam (artificial reservoir) and the land use/land cover (LULC) changes triggered by it physically alter extreme precipitation? The term extreme precipitation (EP) is used as a way of representing the model-derived upper bound of precipitation that pertains to the engineering definition of the standard probable maximum precipitation (PMP) used in design of dams. (2) Among the commonly experienced LULC changes due to dams, which type of change leads to the most detectable alteration of extreme precipitation? The American River Basin (ARW) and the Folsom dam were selected as a study region. Four scenarios of LULC change (comprising also various reservoir surface areas) were analyzed in a step by step fashion to elucidate the scenario leading to most significant impact on EP. The Regional Atmospheric Modeling System (RAMS, version 6.2) was used to analyze the impact of these LULC scenarios in two modes. In the first mode (called normal), the probable precipitation pattern due to each LULC scenario was identified. The second mode (called moisture-maximized), the PMP pattern represented from a 100% relative humidity profile was generated as an indicator of extreme precipitation (EP). For the particular case of ARW and Folsom dam, irrigation was found as having the most detectable impact on EP (a 5% increase in 72 h total for the normal mode and a 3% increase for the moisture-maximized mode) in and around the ARW watershed. Doubling the reservoir size, on the other hand, brought only a small change in EP. Our RAMS-simulated results demonstrate that LULC changes driven by dams can, in fact, alter the local to regional hydrometeorology as well as extreme precipitation. There is a strong possibility of a positive feedback mechanism initiated by irrigated landscapes located upwind of orographic rain producing watersheds that are impounded by large dams.

In the conclusions we wrote

The key goal of our study was to seek answers to two specific science questions: (1) Can a dam (artificial reservoir) and the land use/land cover (LULC) changes triggered by it physically contribute to the modification of extreme precipitation? (2) Among the commonly experienced LULC change due to dams, which type of change leads to the most detectable alteration of extreme precipitation? The answer to our first question is a “yes” while for the second question, we observed that for a dam in which the irrigated land is down- stream and upwind, the irrigation impact is much more superior from the two examined impacts in modifying the extreme precipitation patterns.

This is another in the continuing series of papers by ourselves and other colleagues that document the first-order climate forcing of land use/land cover change.

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New Paper “Decision Scaling: Linking Bottom-Up Vulnerability Analysis With Climate Projections In The Water Sector” By Brown Et Al 2012

I was alerted to a new paper by Faisal Hossain that has adopted part of  the bottom-up perspective with respect that we hae proposed in our article

Pielke, R. A., Sr., R. Wilby,  D. Niyogi, F. Hossain, K. Dairuku,J. Adegoke, G. Kallos, T. Seastedt, and K. Suding (2012), Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective, in Extreme Events and Natural Hazards: The Complexity Perspective, Geophys. Monogr. Ser., vol. 196, edited by A. S. Sharma et al. 345–359, AGU, Washington, D. C., doi:10.1029/2011GM001086. [the article can also be obtained from here]

The new paper is

Brown, C., Y. Ghile, M. Laverty, and K. Li (2012), Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector, Water Resour. Res., 48, W09537, doi:10.1029/2011WR011212.

The abstract reads [highlight added]

There are few methodologies for the use of climate change projections in decision making or risk assessment processes. In this paper we present an approach for climate risk assessment that links bottom-up vulnerability assessment with multiple sources of climate information. The three step process begins with modeling of the decision and identification of thresholds. Through stochastic analysis and the creation of a climate response function, climate states associated with risk are specified. Climate information such as available from multi-GCM, multi run ensembles, is tailored to estimate probabilities associated with these climate states. The process is designed to maximize the utility of climate information in the decision process and to allow the use of many climate projections to produce best estimates of future climate risks. It couples the benefits of stochastic assessment of risks with the potential insight from climate projections. The method is an attempt to make the best use of uncertain but potentially useful climate information. An example application to an urban water supply system is presented to illustrate the process.

The article contains the statement with respect to the top-down global climate model predictions that

A problem with this approach is that GCM projections are relatively poor scenario generators.

I agree, and would add that they have provided NO demonstration of skillful regional and local predictions of changes in climate statistics from that in the historical and paleorecord.

The authors also write

“A novel aspect of the approach is that it uses decision analysis as a framework for characterizing the climate future, and consequently, climate projections, in terms of their position relative to decision thresholds. In doing so, it uses stochastic analysis for risk identification and uses GCM projections for risk estimation, assigning probabilities to hazards, thus linking the two methods.”
 but do, at least, recognize the need to move beyond just the GCM runs. They write
“Appropriately tailored climate information, including GCM projections and stochastically generated conditions from historical and paleodata, and the application of expert judgment, may provide informative answers to this question when approached in the manner described here.”

While the paper still seems to accept the robustness of the global climate model predictions, it does recognize that there are other approaches to accept climate risk.

The article, unfortunately, does not consider other environmental and social risks, relative to climate risks. In the Pielke et al 2012 paper we wrote

“We discuss the adoption of a bottom-up, resource–based vulnerability approach in evaluating the effect of climate and other environmental and societal threats to societally critical resources.This vulnerability concept requires the determination of the major threats to local and regional water, food, energy, human health, and ecosystem function resources from extreme events including climate, but also from other social and environmental issues. After these threats are identified for each resource, then the relative risks can be compared with other risks in order to adopt optimal preferred mitigation/adaptation strategies.

This is a more inclusive way of assessing risks, including from climate variability and climate change than using the outcome vulnerability approach adopted by the IPCC. A contextual vulnerability assessment, using the bottom-up, resource-based framework is a more inclusive approach for policymakers to adopt effective mitigation and adaptation methodologies to deal with the complexity of the spectrum of social and environmental extreme events that will occur in the coming decades, as the range of threats are assessed, beyond just the focus on CO2 and a few other greenhouse gases as emphasized in the IPCC assessments.”

Nonetheless, it is refreshing to see the much-needed start of a movement away from the top-down IPCC approach of assessing risks to key resources, which, as we have shown in our papers and in my weblog posts, is a fundamentally flawed approach.

I have sent the first author a copy of our 2012 paper and hope they will move to the complete adoption of the bottom-up, resource-based perspective.

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News Article “Cool Roofs May Have Side Effects On Regional Rainfall” By Umair Irfan, E&E reporter

Figure from Irfan 2012

There is an E & E Publishing, LLC news article that has appeared. It is by Umair Irfan, E&E reporter titled

Cool roofs may have side effects on regional rainfall

It reads [higlight added]

ClimateWire: Wednesday, October 3, 2012

As desert sands yield to asphalt and concrete, the climate is shifting in Arizona’s “Sun Corridor,” an expanding urban region that includes Phoenix, Tucson, Prescott and Nogales. Researchers are now finding that efforts to offset the climate shift may carry side effects of their own.

Towering buildings, dark roads and sparse vegetation combine to trap heat, making cities warmer than surrounding areas. Previous studies showed that these effects are profound. “What we saw was that urbanization-induced warming is just as important as greenhouse gas-induced climate change,” said Matei Georgescu, an assistant professor in the School of Geographical Sciences and Urban Planning at Arizona State University.

State planners expect the cities in Arizona’s “Sun Corridor” to fuse into a megalopolis by 2050. Click the map for a larger version. Photo courtesy of the University of Arizona.

In a study published last month in the journal Environmental Research Letters, Georgescu demonstrated that these effects change with the seasons and have consequences for regional hydrology, as well. “There’s more to it than just average temperature.”

The Sun Corridor is a good test case, according to Georgescu; it is the fastest-growing “megapolitan” region in the United States. How much population and development growth there will be is uncertain, so Georgescu and his team set a floor and a ceiling for urbanization projections up to the year 2050 based on available data from the Maricopa Association of Governments, the regional agency in charge of long-term planning.

The researchers found that cities would generate the most warming during the summers under the maximum development scenario, with warming exceeding 1 degree Celsius. Under the minimum development projections, warming ranged from 0.1 to 0.3 degrees Celsius for most of the year outside winter.

The models also showed another curious development: Cool roofs — created when developers use reflective paint on rooftops — do perform their intended task of reducing temperatures in urban areas while cutting building energy costs. However, they shift rainfall patterns by reducing evapotranspiration, the process by which water evaporates from the ground and enters the atmosphere. In the maximum expansion scenario, cool roofs led to a 4 percent decline in rainfall.

Modifying CO2 footprint can modify the weather

“Does that suggest that cool roofs are a negative? I think what this leads to is future research to see how they should place cool roofs to minimize impacts,” Georgescu said. “Certain regions might be more appropriate for cool roofs than others.”

Some changes in rain patterns also stem from development itself. “When you put this carpet of urban land use, you’re forbidding the land from capturing and storing the water,” Georgescu said. “We’ve shown in some of our previous work that locally recycled water is very important for regional rainfall.”

Roger Pielke [Sr.], a senior research scientist at the Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder, noted that offsetting or mitigating humanity’s impacts on the world often carries unintended consequences. “Any geoengineering approach will have other effects as well as for the one it is designed to respond to,” he said in an email.

He pointed to research that showed how wind turbines alter regional temperatures even as they reduce carbon emissions that contribute to global climate change. Such trends mean scientists and policymakers will have to factor in how synthetic climate forcers other than greenhouse gases will change temperature, rainfall and weather extremes.

To solve this problem, Pielke suggested measuring environmental variables from a regional scale up to a global scale as a more inclusive way to assess environmental risks than the top-down approach used by the Intergovernmental Panel on Climate Change.

“This vulnerability concept requires the determination of the major threats to local and regional water, food, energy, human health, and ecosystem function resources from extreme events including climate, but also from other social and environmental issues,” he said in a book chapter he co-authored in “Extreme Events and Natural Hazards: The Complexity Perspective” earlier this year.

For now, Georgescu said, he will concentrate on regional modeling because global climate models do not yet offer enough resolution to illuminate climate trends in areas like the Sun Corridor. Conducting similar studies in multiple regions around the world could help climate modelers improve their global projections and help planners anticipate local climate shifts.

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New Article “Special Section On Climate Change And Water Resources: Climate Nonstationarity And Water Resources Management.” By Salas Et Al 2012

Jose (Pepe) Salas of Colorado State University has alerted us to an important new paper that he has authored. It is

Salas, J., Rajagopalan, B., Saito, L., and Brown, C. (2012). ”Special Section on Climate Change and Water Resources: Climate Nonstationarity and Water Resources Management.” J. Water Resour. Plann. Manage., 138(5), 385–388. doi: 10.1061/(ASCE)WR.1943-5452.0000279

The first paragraph of the article reads [highlight added]

Over the past three decades, hydrologists and water resources specialists have been concerned with the issue of nonstationarity arising from several factors. First is the effect of human intervention on the landscape that may cause changes in the precipitation–runoff relationships at various temporal and spatial scales. Second is the occurrence of natural events such as volcanic explosions or forest fires that may cause changes in the composition of the air, the soil surface, and geomorphology. Third is the low-frequency component of oceanic–atmospheric phenomena that may have significant effects on the variability of hydrological processes such as annual runoff, peak flows, and droughts. Fourth is global warming, which may cause changes to oceanic and atmospheric processes, thereby affecting the hydrological cycle at various temporal and spatial scales. There has been a significant amount of literature on the subject and thousands of research and project articles and books published in recent decades.

Among the informative text in the article, I was pleased to see their further confirmation of land use/land cover changes as a first-order climate forcing, when they wrote

Examples of human intrusion on the landscape are the changes in land use resulting from agricultural developments in semiarid and arid lands (e.g., Pielke et al. 2007, 2011), changes caused by large-scale deforestation (e.g., Gash and Nobre 1997), changes resulting from open-pit mining operations (e.g., Salas et al. 2008), and changes from increasing urbanization in watersheds (e.g., Konrad and Booth 2002,Villarini et al. 2009)…..Large-scale landscape changes such as deforestation in the tropical regions can potentially alter atmospheric circulation patterns, and consequently affect global weather and climate (e.g., Lee et al. 2008, 2009).

With respect to natural forcings and feedbacks, they write

Major natural events, such as the volcanic explosion of Mount St. Helens in 1980 or the El Chichon volcanic explosion of 1982 induce a shock to the climate system in the form of global cooling that continues for several years. These events can also affect global circulation. Low-frequency climate drivers of the oceanic– atmospheric system such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) modulate global climate at interannual and multidecadal time scales. These drivers are the main sources of nonstationarity in global climate and hydrology. Large numbers of papers documenting the effect of these drivers on global hydroclimatology continue to emerge (e.g., Dilley and Heyman 1995; Mantua et al. 1997; Enfield et al. 2001; Akintug and Rasmussen 2005; Hamlet et al. 2005).

With respect to “global warming“, they write

In addition to climate variability and change due to the previously mentioned factors, anthropogenic warming of the oceans and atmosphere because of increased greenhouse gas concentrations and the ensuing changes to the hydrologic cycle are topics of serious pursuit. The international scientific community is making strides in understanding the potential warming and its effects on all aspects of climate variability [Intergovernmental Panel on Climate Change (IPCC) 2007], but the impacts on the hydrologic cycle remain debatable and inconclusive (e.g., Cohn and Lins 2005; Legates et al. 2005; Hirsch and Ryberg 2011). Based on analyses of the global mean CO2 (GMCO2) and annual flood records in the United States, no strong statistical evidence for flood magnitudes increasing with GMCO2 increases were found (Hirsch and Ryberg 2011). Although general circulation models have had success in the attribution of warming global temperatures to anthropogenic causes, their credibility and utility in reproducing variables that are relevant to hydrology and water resources applications is less clear. For example, the IPCC Report for Latin America acknowledges that “the current GCMs do not produce projections of changes in the hydrological cycle at regional scales with confidence. In particular the uncertainty of projections of precipitation remain high….That is a great limiting factor to the practical use of such projections for guiding active adaptation or mitigation policies” (Magrin et al. 2007; Boulanger et al. 2007).

It is refreshing to see this broader perspective being adopted by the hydrology community.

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Comment Submitted To BAMS On The Mearns Et Al 2012 Paper

As discussed in my posts

Follow Up On My E-Mail Request To Linda Mearns Of NCAR

“The North American Regional Climate Change Assessment Program: Overview of Phase I Results” By Mearns Et Al 2012 – An Excellent Study But It Overstates Its Significance In The Multi-Decadal Prediction Of Climate

E-Mail To Linda Mearns On The 2012 BAMS Article On Dynamic Downscaling

there is a significant overstatement of the implications of the paper

Linda O. Mearns, Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.

Linda is not interested in discussing this outside of a formal Comment to BAMS in which she and her co-authors can reply.  Thus I have submitted a Comment and reproduced it below. BAMS usually takes quite a while to complete the Comment/Reply process and I will post (and respond on my weblog further if needed) when this publication process is complete. Before I post my Comment, however, I want to alert readers to Judy Curry’s post from today titled

RS Workshop on Handling Uncertainty in Weather & Climate
Prediction. Part I

where she wrote

Regional climate change:

  • Little to no skill here; increased  resolution not helping
  • Dynamical & statistical downscaling adds little value
  • Many extreme weather events not  explicitly simulated
  • Depends on poorly simulated modes of  natural internal variability

GCMs are currently incapable of simulating:

  • Regional climate variability and change
  • Network of teleconnection climate regimes on DEC-CEN timescales
  • Predictions of emergent phenomena, e.g. abrupt climate change

It is unlikely that the current path of development will improve this

which is in complete agreement with my view on this topic. Following is my Comment submission to BAMS

Comment On Mearns et al 2012

Abstract

The  Mearns et al 2012 BAMS paper with respect to  downscaling from reanalyses it is an important new contribution. However, its claim of that these results can provide useful information about climate change is inappropriate and misleading to the impacts and policy communities.

The Comment

The Mearns et al (2012) article provides documentation of the level of skill of one type of dynamic downscaling. Within that framework it is an important new contribution which will be widely cited. However, the paper only provides, at best, an upper bound of what is possible with respect to their goal to provide to provide

uncertainties in regional scale projections of future climate and produce high resolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere ocean general circulation models (AOGCMs) forced with the A2 SRES scenario.”

The Mearns et al 2012 study concludes with the claim that

“….. We have shown that all the models can simulate aspects of climate well, implying that they all can provide useful information about climate change. In particular, the results from phase I of NARCCAP will be used to establish uncertainty due to boundary conditions as well as final weighting of the models for the development of regional probabilities of climate change.”

However, this conclusion significantly overstates the significance of their findings in terms of its application to the multi-decadal prediction of regional climate (i.e. “climate change”). The Mearns et al study uses observational data (from a reanalysis) to drive the regional models. Using the classification we have introduced in Castro et al (2005), Mearns et al is a Type 2 dynamic downscaling study.

As we wrote in Pielke and Wilby (2011)

“Type 2dynamic downscaling refers to regional weather (or climate) simulations…in which the regional model’s initial atmospheric conditions are forgotten…..but results still depend on the lateral boundary conditions from a global numerical weather prediction where initial observed atmospheric conditions are not yet forgotten or are from a global reanalysis.….Downscaling from reanalysis products (Type 2 downscaling) defines the maximum forecast skill that is achievable with Type 3 and Type 4 downscaling.”

while

“Type 4 dynamic downscaling takes lateral boundary conditions from an Earth system model in which coupled interactions among the atmosphere, ocean, biosphere, and cryosphere are predicted ……Other than terrain, all other components of the climate system are calculated by the model except for human forcings, including greenhouse gas emissions scenarios, which are prescribed. Type 4 dynamic downscaling is widely used to provide policy makers with impacts from climate decades into the future……. Type 4 downscaling has practical value but with the very important caveat that it should be used for model sensitivity experiments and not as predictions [e.g., Pielke, 2002; Prudhomme et al., 2010].”

As discussed in Pielke and Wilby, Type 1downscaling is used for short-term, numerical weather prediction, while Type 3 dynamic downscaling takes lateral boundary conditions from a global model prediction forced by specified real world surface boundary conditions such as seasonal weather predictions based on observed sea surface temperatures. Because real-world observational constraints diminish from Type 1 to Type 4 downscaling, uncertainty grows as more climate variables must be predicted by models, rather than obtained from observations.

One cannot, therefore, use Type 2 downscaling to make claims, as Mearns et al have, about the accuracy of Type 4 downscaling. Type 2 downscaling provides a real world observational constraint on how much the regional model can diverge from reality. This is not the case with Type 4 downscaling. A Type 4 downscaling cannot be more accurate than a Type 2 downscaling.

A more appropriate approach is to first assess what changes in climate statistics would have to occur in order to cause a negative impact to key resources, as we recommend in Pielke et al 2012. Only then assess what is plausibly possible and how to mitigate/adapt to prevent a negative effect from occurring.

The type of downscaling used in a study is a critically important point that needs to be emphasized when dynamic downscaling studies are presented. Mearns et al (2012) did not do this.

Indeed, Mearns et al 2012 is a study of the current climate, not of changes in climate statistics over the time period of the model runs. The Mearns et al 2012 study did not look at the issue of their skill to predict changes in climate statistics. Even reproducing the current regional climate in a hindcast mode when the results are not constrained by reanalyses is being shown to be a daunting challenge; e.g.  Xu et al 2012; Fyfe et al 2011; van Oldenborgh et al 2012; Anagnostopoulos et al 2010; Stephens et al 2010; Sun et al 2012; van Haren et al 2012; Kundzewicz et al 2010; Goddard el al 2012; Driscoll et al 2012; Mauritsen et al 2012; Jiang et al 2012.

It is even more challenging to skillfully predict changes in regional climate which is what is required if the RCMs are to add any value for predicting climate in the coming decades beyond what could be extracted from reanalyses.  The Mearns et al 2012 paper is, therefore, misleading the impacts communities by indicating that their results apply to regional climate change (i.e. Type 4 downscaling).

In summary, the Mearns et al 2012 BAMS paper with respect to Type 2 downscaling it is an important new contribution. However, its application to climate change runs (Type 4 downscaling) is inappropriate and is misleading to the impacts and policy communities on a level of predictive skill that does not yet exist.

References

Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110

Castro, C.L., R.A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res. – Atmospheres, 110, No. D5, D05108, doi:10.1029/2004JD004721.

Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.

Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn (2011), Regional climate models add value to global model data—A review and selected examples, Bull. Am. Meteorol. Soc., 92, 1181–1192, doi:10.1175/2011BAMS3061.1.

Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W.-S. Lee, and K. von Salzen (2011), Skillful predictions of decadal trends in global mean surface temperature, Geophys. Res. Lett.,38, L22801, doi:10.1029/2011GL049508

Goddard, A. Kumar, A. Solomon, D. Smith, G. Boer, P. Gonzalez, V. Kharin, W. Merryfield, C. Deser, S. J. Mason, B. P. Kirtman, R. Msadek, R. Sutton, E. Hawkins, T. Fricker, G. Hegerl, C. A. T. Ferro, D. B. Stephenson, G. A. Meehl, T. Stockdale, R. Burgman, A. M. Greene, Y. Kushnir, M. Newman, J. Carton, I. Fukumori, T. Delworth. (2012) A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics Online publication date: 24-Aug-2012.

Jiang, J. H., et al. (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations, J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237. published 18 July 2012.

Kundzewicz, Z. W., and E.Z. Stakhiv (2010) Are climate models “ready for prime time” in water resources managementapplications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.

Mauritsen, T., et al. (2012), Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, doi:10.1029/2012MS000154. published 7 August 2012.

Mearns, Linda O. , Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.

Pielke, R. A., Sr., (2002), Overlooked issues in the U.S. national climate and IPCC assessments, Clim. Change, 52(1-2), 1–11, doi:10.1023/ A:1017473207687.

Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008.

Pielke, R. A., Sr., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku,J. Adegoke, G. Kallos, T. Seastedt, and K. Suding (2012), Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective, in Extreme Events and Natural Hazards: The Complexity Perspective, Geophys. Monogr. Ser., vol. 196, edited by A. S. Sharma et al. 345.359, AGU, Washington, D. C., doi:10.1029/2011GM001086.

Prudhomme, C., R. L. Wilby, S. Crooks, A. L. Kay, and N. S. Reynard (2010), Scenario-neutral approach to climate change impact studies: Application to flood risk, J. Hydrol., 390, 198–209, doi:10.1016/ j .jhydrol .2010.06.043.

Sakaguchi, K., X. Zeng, and M. A. Brunke (2012), The hindcast skill of the CMIP ensembles for the surface air temperature trend, J. Geophys. Res., 117, D16113, doi:10.1029/2012JD017765. published 28 August 2012

Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

Sun, Z., J. Liu, X. Zeng, and H. Liang (2012), Parameterization of instantaneous global horizontal irradiance at the surface. Part II: Cloudy-sky component, J. Geophys. Res., doi:10.1029/2012JD017557, in press.

van Haren, Ronald, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, 2012: SST and circulation trend biases cause an underestimation of European precipitation trends Climate Dynamics 2012, DOI: 10.1007/s00382-012-1401-5

van Oldenborgh, G.J., F.J. Doblas-Reyes, B. Wouters, W. Hazeleger (2012): Decadal prediction skill in a multi-model ensemble. Clim.Dyn. doi:10.1007/s00382-012-1313-4

Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1

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New Paper “An Empirical Study Of The Impact Of Human Activity On Long-Term Temperature Change In China: A Perspective From Energy Consumption” By Li And Zhao 2012

Figure from Li and Zhao (2012) –  Spatial distribution of high, mid and low energy consumption region in China. Data for Tibet and Taiwan are absent. Green spot is the provincial capital cities of China.

Jos de Laat has alerted us to a new paper. It is

Li, Y. and X. Zhao (2012), An empirical study of the impact of human activity on long-term temperature change in China: A perspective from energy consumption, J. Geophys. Res., 117, D17117, doi:10.1029/2012JD018132.

The abstract reads [highlight added]

Human activity is an important contributor to local temperature change, especially in urban areas. Energy consumption is treated here as an index of the intensity of human induced local thermal forcing. The relationship between energy consumption and temperature change is analyzed in China by Observation Minus Reanalysis (OMR) method. Temperature trends for observation, reanalysis and OMR are estimated from meteorological records and 2 m-temperature from NCEP/NCAR Reanalysis 1 for the period 1979–2007. A spatial mapping scheme based on the spatial and temporal relationship between energy consumption and Gross Domestic Production (GDP) is developed to derive the spatial distribution of energy consumption of China in 2003. A positive relationship between energy consumption and OMR trends is found in high and mid energy consumption region. OMR trends decline with the decreasing intensity of human activity from 0.20°C/decade in high energy consumption region to 0.13°C/decade in mid energy consumption region. Forty-four stations in high energy consumption region that are exposed to the largest human impact are selected to investigate the impact of energy consumption spatial pattern on temperature change. Results show human impact on temperature trends is highly dependent on spatial pattern of energy consumption. OMR trends decline from energy consumption center to surrounding areas (0.26 to 0.04°C/decade) and get strengthened as the spatial extent of high energy consumption area expands (0.14 to 0.25°C/decade).

Excerpts from this paper include

Besides the impact of land use change on climate, the thermal impact induced by human activity within city plays significant role and should not be ignored. One of them is the anthropogenic heat released from energy consumption. Several studies have shown that anthropogenic heat is important to the development of UHI. Simulation results from a case study in Philadelphia suggested that anthropogenic heat contributes about 2~3C to the nighttime heat island in winter [Fan and Sailor, 2005].

The conclusion contains the text

Our results show significant warming has occurred for most stations in China and the magnitude of warming is closely related to energy consumption, which represents the intensity of human activity. For high and mid energy consumption group, OMR trends decline with the decrease of energy consumption. OMR trends for high and mid energy consumption group is 0.20 and 0.13C/decade respectively. Stronger warming is observed for station with high energy consumption, which usually locates in or near cities. Therefore, the strong warming is more likely a consequence of the local thermal forcing induced by human activity.

It seems that stations belong to high and mid energy consumption group in this study are affected
by human impact to a discernible extent. Just as De Laat[2008] demonstrated, anthropogenic heat released from energy consumption may very well have contributed to the observed temperature change patterns.Thus, it may raise more attention to consider the influence of human activity on surface temperature records in the past and next decades.

This study provides even more motivation for Anthony Watts to expand his station siting quality project to the entire globe!

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Follow Up On My E-Mail Request To Linda Mearns Of NCAR

source of image from the NARCCAP website

Last week I posted twice on the BAMS article

Linda O. Mearns, Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.

in

“The North American Regional Climate Change Assessment Program: Overview of Phase I Results” By Mearns Et Al 2012 – An Excellent Study But It Overstates Its Significance In The Multi-Decadal Prediction Of Climate

E-Mail To Linda Mearns On The 2012 BAMS Article On Dynamic Downscaling

I have had an e-mail response from Linda Mearns and she informed me that she is too busy to respond at this time. I have requested her permission to post her e-mails rejecting my request and will present if she gives me permission.

Quite frankly, I am disappointed as ignoring the issues that I summarized in my two posts is not the proper approach to advancing climate science.

I have succinctly summarized below what are the fundamental flaws in the use of regional downscaling with respect to multi-decadal climate predictions:

My Conclusions:

  • The  Mearns et al 2012 BAMS paper with respect to type 2 downscaling it is an important new contribution.
  • However, it’s application to climate change runs (type 4 downscaling) is inappropriate and misleading to the impacts and policy communities.

In order to refute the second conclusion, these following two questions must be answered in the affirmative:

1. Can a type 4 downscaling can be more accurate than a type 2 downscaling? Otherwise why not just start from regional reanalyses and assess what changes would have to occur in order to cause a negative impact to key resources, as we recommed in Pielke et al 2012.  Only then assess what is plausibly possible and how to mitigate/adapt to prevent a negative effect from occuring.

2. Have the regional climate models shown skill  in predicting changes over time in regional multi-decadal regional climate statistics?

My answer to both #1 and #2 are NO.

The  Mearns et al 2012 BAMS paper uses observed data, as processed through reanalyses, as lateral boundary conditions, and interior nudging when used. This provides a real world constraint on how much the regional model can diverge from reality. This is why we label it as type 2 downscaling.

The results of the Mearns et al 2012 BAMS paper cannot be used to justify providing changes in climate statistics to the impacts communities (i.e. through type 4 downscaling).

The actual ability of climate models to predict (in hindcast) EVEN the current climate is very limited. I documented this with a number of peer-reviewed papers in my posts

More CMIP5 Regional Model Shortcomings

CMIP5 Climate Model Runs – A Scientifically Flawed Approach.

The Hindcast Skill Of The CMIP Ensembles For The Surface Air Temperature Trend –  By Sakaguchi Et Al 2012.

Predicting “climate change” is  even more of a challenge. The climate models have shown NO skill at predicting CHANGES in regional climate statistics.

It may be convenient to ignore these issues in order to keep the grant and contract money flowing, but unless these fundamental flaws can be refuted, research money and time is being wastefully spent.

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Comment On “A National Strategy for Advancing Climate Modeling” From The NRC

There is a new and, in my view, scientifically flawed report published by the National Research Council. The report is

 A National Strategy for Advancing Climate Modeling

I have a few comments on this report in my post today which document its failings. First, the overarching perspective of the authors of the NRC report is [highlight added]

As climate change has pushed climate patterns outside of historic norms, the need for detailed projections is growing across all sectors, including agriculture, insurance, and emergency preparedness planning. A National Strategy for Advancing Climate Modeling emphasizes the needs for climate models to evolve substantially in order to deliver climate projections at the scale and level of detail desired by decision makers, this report finds. Despite much recent progress in developing reliable climate models, there are still efficiencies to be gained across the large and diverse U.S. climate modeling community.

My Comment:

First, their statement that “….climate change has pushed climate patterns outside of historic norms” is quite a convoluted statement. Climate has always been changing. This insertion of “climate change” clearly is a misuse of the terminology “climate change” as I discussed in the post

The Need For Precise Definitions In Climate Science – The Misuse Of The Terminology “Climate Change”

Second, there are no reliable climate model predictions on multi-decadal time scale! This is clearly documented in the posts; e.g. see

Comments On The Nature Article “Afternoon Rain More Likely Over Drier Soils” By Taylor Et Al 2012 – More Rocking Of The IPCC Boat

More CMIP5 Regional Model Shortcomings

CMIP5 Climate Model Runs – A Scientifically Flawed Approach

The NRC Report also writes

Over the next several decades, climate change and its myriad consequences will be further unfolding and possibly accelerating, increasing the demand for climate information. Society will need to respond and adapt to impacts, such as sea level rise, a seasonally ice-free Arctic, and large-scale ecosystem changes. Historical records are no longer likely to be reliable predictors of future events; climate change will affect the likelihood and severity of extreme weather and climate events, which are a leading cause of economic and human losses with total losses in the hundreds of billions of dollars over the past few decades.

My Comment:

As I wrote earlier in this post, the multi-decadal climate model predictions have failed to skillfully predict not only changes in climate statistics over the past few decades, but cannot even accurately enough simulate the time averaged regional climates! Moreover, in terms of the comment that

“…climate change will affect the likelihood and severity of extreme weather and climate events, which are a leading cause of economic and human losses with total losses in the hundreds of billions of dollars over the past few decades.”

this is yet another example of where the BS meter is sounding off! See, for example, my son’s most recent discussion of this failing by the this climate community;

The IPCC sinks to a new low

The NRC report continues

Computer models that simulate the climate are an integral part of providing climate information, in particular for future changes in the climate. Overall, climate modeling has made enormous progress in the past several decades, but meeting the information needs of users will require further advances in the coming decades.

They also write that

Climate models skillfully reproduce important, global-to-continental-scale features of the present climate, including the simulated seasonal-mean surface air temperature (within 3°C of observed (IPCC, 2007c), compared to an annual cycle that can exceed 50°C in places), the simulated seasonal-mean precipitation (typical errors are 50% or less on regional scales of 1000 km or larger that are well resolved by these models [Pincus et al., 2008]), and representations of major climate features such as major ocean current systems like the Gulf Stream (IPCC, 2007c) or the swings in Pacific sea-surface temperature, winds and rainfall associated with El Niño (AchutaRao and Sperber, 2006; Neale et al., 2008). Climate modeling also delivers useful forecasts for some phenomena from a month to several seasons ahead, such as seasonal flood risks.

My Comment:  Actually “climate modeling” has made little progress in simulating regional climate on multi-decadal time scales, and no demonstrated evidence of being able to skillfully predict changes in the climate system. Indeed, the most robust work are the peer-reviewed papers that are in my posts (as I also listed earlier in this post)

Comments On The Nature Article “Afternoon Rain More Likely Over Drier Soils” By Taylor Et Al 2012 – More Rocking Of The IPCC Boat

More CMIP5 Regional Model Shortcomings

CMIP5 Climate Model Runs – A Scientifically Flawed Approach

which document the lack of skill in the models.

The report also defines “climate” as

Climate is conventionally defined as the long-term statistics of the weather (e.g., temperature, precipitation, and other meteorological conditions) that characteristically prevail in a particular region.

Readers of my weblog should know that this is an inappropriately narrow definition of climate. In the NRC report

National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp.

(which the new NRC report conveniently ignored), climate is defined as

The system consisting of the atmosphere, hydrosphere, lithosphere, and  biosphere, determining the Earth’s climate as the result of mutual interactions  and responses to external influences (forcing). Physical, chemical, and  biological processes are involved in interactions among the components of the  climate system.

FIGURE 1-1 The climate system, consisting of the atmosphere, oceans, land, and cryosphere. Important state variables for each sphere of the climate system are listed in the boxes. For the purposes of this report, the Sun, volcanic emissions, and human-caused emissions of greenhouse gases and changes to the land surface are considered external to the climate system (from NRC, 2005)

This new NRC report “A National Strategy for Advancing Climate Modeling” misrepresents the capabilities of the climate models to simulate the climate system on multi-decadal time periods.

While I am in support of studies that assess the predictability skill of the models and to use them for monthly and seasonal predictions (which can be quickly tested against observations), seeking to advance climate modeling by claiming that more accurate multi-decadal regional forecasts can be made for policymakers and impact scientists and engineer with their proposed approach is, in my view, a dishonest communication to policymakers and to the public.

This need for advanced climate modeling should be promoted only and specifically with respect to assessing predictability on monthly,seasonal and longer time scales, not to making multi-decadal predictions for the impacts communties.

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