Surrogate Climate Research Articles

Summer dryness in a warmer climate: a process study with a regional climate model

Earlier GCM studies have expressed the concern that an enhancement of greenhouse warming might increase the occurrence of summer droughts in mid-latitudes, especially in southern Europe and central North America. This could represent a severe threat for agriculture in the regions concerned, where summer is the main growing season. These predictions must however be considered as uncertain, since most studies featuring enhanced summer dryness in mid-latitudes use very simple representations of the land-surface processes ("bucket" models), despite their key importance for the issue considered. The current study uses a regional climate model including a land-surface scheme of intermediate complexity to investigate the sensitivity of the summer climate to enhanced greenhouse warming over the American Midwest. A surrogate climate change scenario is used for the simulation of a warmer climate. The control runs are driven at the lateral boundaries and the sea surface by reanalysis data and observations, respectively. The warmer climate experiments are forced by a modified set of initial and lateral boundary conditions. The modifications consist of a uniform 3 K temperature increase and an attendant increase of specific humidity (unchanged relative humidity). This strategy maintains a similar dynamical forcing in the warmer climate experiments, thus allowing to investigate thermodynamical impacts of climate change in comparative isolation. The atmospheric CO2 concentration of the sensitivity experiments is set to four times its pre-industrial value. The simulations are conducted from March 15 to October 1st, for 4 years corresponding to drought (1988), normal (1986, 1990) and flood (1993) conditions. The numerical experiments do not present any great enhancement of summer drying under warmer climatic conditions. First, the overall changes in the hydrological cycle (especially evapotranspiration) are of small magnitude despite the strong forcing applied. Second, precipitation increases in spring lead to higher soil water recharge during this season, compensating for the enhanced soil moisture depletion occurring later in the year. Additional simulations replacing the plant control on transpiration with a bucket-type formulation presented increased soil drying in 1988, the drought year. This suggests that vegetation control on transpiration might play an important part in counteracting an enhancement of summer drying when soil water gets limited. Though further aspects of this issue would need investigating, our results underline the importance of land-surface processes in climate integrations and suggest that the risk of enhanced summer dryness in the region studied might be less acute than previously assumed, provided the North American general circulation does not change markedly with global warming.

Are temperature‐sensitive proxies adequate for North Atlantic Oscillation reconstructions?

Reconstructions of the North Atlantic Oscillation Index (NAOI) for the last centuries are mostly based on statistical models linking this index with proxy records. The underlying assumption is that the relationship between the NAO and the proxy records is stable in time and independent of time scale. This assumption might not be physically substantiated, since at time scales of centuries, other processes, such as solar variability, might disturb the link between the NAOI and temperature‐sensitive indicators. The statistical approach for NAOI reconstructions was tested using a climate simulation with a climate model driven by the external forcing of the last 490 years, as a surrogate climate. Two kinds of indicators were tested, air temperature and precipitation. It was found that the NAOI reconstructions based on precipitation are more reliable than the reconstructions based on temperature. Furthermore, the choice of geographical box has a non‐negligible influence on the reconstructed NAOI.

Satellite climatology and the environmental risk of Schistosoma mansoni in Ethiopia and east Africa

Annual and seasonal composite maps prepared from the normalized difference vegetation index (NDVI) and earth surface maximum temperature ( T max) satellite data from the archives of the Global land 1-km program of the United States Geological Survey (USGS) were studied for. their potential value, using geographic information system (GIS) methods, as surrogates of climate data in the development of environmental risk models for schistosomiasis in Ethiopia. Annual, wet season and dry season models were developed and iteratively analyzed for relationships with Schistosoma mansoni distribution and infection prevalence rates. Model-predicted endemic area overlays that best fit the distribution of sites with over 5% prevalence corresponded to values of NDVI 125–145 and T max 20–33°C in the annual composite map, NDVI 125–145 and T max 18–29°C for the wet season map, and NDVI 125–140 and T max 22–37°C for the dry season map. The model-predicted endemic area was similar to that of a prior model developed using an independent agroecologic zone data set from the United Nations Food and Agriculture Organization (FAO). Results were consistent with field and laboratory data on the preferences and limits of tolerance of the S. mansoni– Biomphalaria pfeifferi system. Results suggest that Global 1-km NDVI and T max, when used together, can be used as surrogate climate data for development of GIS risk assessment models for schistosomiasis. The model developed for Ethiopia based on global 1-km satellite data was extrapolated to a broader area of East Africa. When used with FAO agroecologic zone climate data limits of <27°C for average annual mean temperature and annual moisture deficits (annual rain–annual potential evapotranspiration) of <−1300 mm, the model accurately represented the regional distribution of the S. mansoni– B. pfeifferi system in the East Africa extrapolation area.

Interpretation of Snow-Climate Feedback as Produced by 17 General Circulation Models

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models

Understanding the cause of differences among general circulation model projections of carbon dioxide-induced climatic change is a necessary step toward improving the models. An intercomparison of 14 atmospheric general circulation models, for which sea surface temperature perturbations were used as a surrogate climate change, showed that there was a roughly threefold variation in global climate sensitivity. Most of this variation is attributable to differences in the models' depictions of cloud-climate feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as climatic predictors.