Category Archives: Assessment of climate predictability

New Paper The Impact Of Spring Subsurface Soil Temperature Anomaly In The Western U.S. On North American Summer Precipitation: A Case Study Using Regional Rlimate Model Downscaling” By Xue Et al 2012

There is an important new regional climate model paper (using type 3 downscaling as defined in

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.

The new paper is

Xue, Y., R. Vasic, Z. Janjic, Y. M. Liu, and P. C. Chu (2012), The impact of spring subsurface soil temperature anomaly in the western U.S. on North American summer precipitation: A case study using regional climate model downscaling, J. Geophys. Res., 117, D11103, doi:10.1029/2012JD017692.

The abstract reads [highlight added]

This study explores the impact of spring subsurface soil temperature (SUBT) anomaly in the western U.S. on North American summer precipitation, mainly southeastern U.S., and possible mechanisms using a regional climate Eta model and a general circulation model (GCM). The GCM produces the lateral boundary condition (LBC) for the Eta model. Two initial SUBT conditions (one cold and another warm) on May 1st were assigned for the GCM runs and the corresponding Eta runs. The results suggest that antecedent May 1st warm initial SUBT in the western U.S. contributes positive June precipitation over the southern U.S. and less precipitation to the north, consistent with the observed anomalies between a year with a warm spring and a year with a cold spring in the western U.S. The anomalous cyclone induced by the surface heating due to SUBT anomaly propagated eastward through Rossby waves in westerly mean flow. In addition, the steering flow also contributed to the dissipation of perturbation in the northeastern U.S. and its enhancement in southeastern U.S. However, these results were obtained only when the Eta model run was driven by the corresponding GCM run. When the same reanalysis data were applied for both (cold and warm initial SUBT) Eta runs’ LBCs, the precipitation anomalies could not be properly produced, indicating the intimate dependence of the regional climate sensitivity downscaling on the imposed global climate forcing, especially when the impact was through wave propagation in the large-scale atmospheric flow.

Excerpts from the conclusion reads

This study demonstrates that although GFS runs with large internal variability and coarse resolutions were unable to produce adequate precipitation difference patterns, the downscaling of GFS precipitation output using Eta did yield significant results consistent with observations.

The Eta results were obtained only when we used GFS outputs for the corresponding Eta runs’ LBCs. When we applied the same reanalysis data for both (control and sensitivity) Eta runs’ LBCs, the Rossby wave propagation was suppressed and observed precipitation anomalies were not properly produced. Because large scale circulation and low-level moisture transfer played crucial roles in proper simulations of the U.S. summer precipitation, maintaining the same LBC produced similar large-scale patterns, causing severe limitations in this sensitivity study. Downscaled regional climate is closely linked to the imposed global climate forcing. Therefore, for climate sensitivity studies using RCMs, consistent lateral boundary forcing may be crucial, especially when the impact is produced through wave transference in the atmosphere.

This is the first modeling study to explore the western U.S. SUBT impact and its teleconnections with Eastern U.S. precipitation. The results suggest that SUBT may be able to provide an extended element of memory, which would enhance predictability. However, there are many issues which require more investigations……

The Xue et al 2012 paper also confirms what we presented in

Pielke Sr., R.A., G.E. Liston, J.L. Eastman, L. Lu, and M. Coughenour,  1999: Seasonal weather prediction as an initial value problem. J. Geophys.  Res., 104, 19463-19479.

The abstract of our paper reads

Using a climate version of a regional atmospheric model, we show that the seasonal evolution of weather is dependent on the initial soil moisture and landscape specification. Coupling this model to a land-surface model, the soil moisture distribution and landscape are shown to cause a significant nonlinear interaction between vegetation growth and precipitation. These results demonstrate that seasonal weather prediction is an initial value problem. Moreover, on seasonal and longer timescales the surface characteristics such as soil moisture, leaf area index, and landcover type must be treated as dynamically evolving dependent variables, instead of prescribed parameters.

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New Paper “Skill In The Trend And Internal Variability In A Multi-Model Decadal Prediction Ensemble” By Oldenborgh El Al 2012

In my posts, I have urged that the focus of climate modeling research change from focusing on providing multi-decadal climate predictions to the assessment of predictability; e.g. see

The Difference Between Prediction and Predictability – Recommendations For Research Funding Related to These Distinctly Different Concepts

I was alerted by Jos de Laat of KNMI to an important new research paper that specifically addresses this issue. This paper is

Oldenborgh, G.J. van, F.J. Doblas-Reyes, B. Wouters and W. Hazeleger,  2012: Skill in the trend and internal variability in a multi-model decadal prediction ensemble.  accepted, Clim. Dyn.

The abstract [as it reads here] is [highlight added]

Decadal climate predictions have skill due to predictable components in boundary conditions (mainly greenhouse gases) and initial conditions (mainly the ocean). We investigated the skill of temperature and precipitation hindcasts from a set of four coupled ocean-atmosphere models. Regional variations in skill with and without trend due to global warming point to separate effects of the boundary forcing and the ocean initial state. In temperature most skill comes from the prescribed boundary forcing. The trend of the global mean temperature is represented well in the hindcasts, but variations around the trend show little skill. The models have non-trivial skill in hindcasts of North Atlantic SST beyond the trend. The same may hold for the decadal ENSO region, although the signal is less clear. Hence we conclude that the ocean initial state contributes significantly to skill in forecasting SST in these regions.

The conclusion contains the text

A 4-model 12-member ensemble of 10-yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation. The main source of skill in temperature is the trend, which is primarily forced by greenhouse gases and aerosols. This trend contributes almost everywhere to the skill. Variation in the global mean temperature around the trend do not have any skill beyond the first year. However, regionally there appears to be skill beyond the trend in the two areas of well-known low-frequency variability: SST in parts of the North Atlantic and Pacific Oceans is predicted better than persistence. A comparison with the CMIP3 ensemble shows that the skill in the northern North Atlantic and eastern Pacific is most likely due to the initialisation, whereas the skill in the subtropical North Atlantic and western North Pacific are probably due to the forcing.

In the Atlantic, the ensemble shows clear skill in predicting an AMO index that is orthogonal to the trend in yrs 2–5, and reasonable skill in yrs 6–9. The skill in decadal ENSO is lower, not statistically significant, but in agreement with other studies. The CMIP3 ensemble shows less skill in both these indices. There is also an indication of skill in hindcasting decadal Sahel rainfall variations, which are known to be teleconnected to North Atlantic and Pacific SST. The uninitialised CMIP3 ensemble that includes volcanic aerosols reproduces these variations as well, but the models without volcanic aerosols do not. It therefore remains an open question whether initialisation improves predictions of Sahel rainfall.

The modelled trends agree well with observations in the global mean, but the agreement is not so good at the local scale.

These experiments are only a first step towards decadal forecasting using non-optimised methods from seasonal forecasting. The skill assessment does not take into account the considerable biases and drift of the models. It is based on only nine or ten data points and hence suffers from large statistical uncertainties. Larger ensembles sizes per model and more frequent and earlier starting dates will be required to characterise the skill of decadal forecasts better. The verification of decadal hindcasts can then be used to improve the climate models, their forcings and initialisation procedures to give more reliable and skilful climate forecasts.

The authors should be commended for focusing on this assessment of predictability. We need more such excellent studies! 

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Filed under Assessment of climate predictability, Climate Change Forcings & Feedbacks, Research Papers