General Circulation Model Predictions Research Articles

Climate change and Aedes albopictus risks in China: current impact and future projection

BackgroundFuture distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models (GCMs). However, it is difficult to validate the GCM results and assess the uncertainty of the predictions. The observed changes in climate may be very different from the GCM results. We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.MethodsWe collected Ae. albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021. We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses. We analyzed the relationship between climatic variables and the prevalence of Ae. albopictus in different months/seasons. We built a classification tree model (based on the average of 999 runs of classification and regression tree analyses) to predict the monthly/seasonal Ae. albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae. albopictus distribution. Using these models, we projected the future distributions of Ae. albopictus for 2050 and 2080.ResultsThe study included Ae. albopictus surveillance from 259 sites in China found that winter to early spring (November–February) temperatures were strongly correlated with Ae. albopictus prevalence (prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence, while precipitation in summer (June–September) was important predictor for Ae. albopictus prevalence. The machine learning tree models predicted the current prevalence of Ae. albopictus with high levels of agreement (accuracy > 90% and Kappa agreement > 80% for all 12 months). Overall, winter temperature contributed the most to Ae. albopictus distribution, followed by summer precipitation. An increase in temperature was observed from 1970 to 2021 in most places in China, and annual change rates varied substantially from -0.22 ºC/year to 0.58 ºC/year among sites, with the largest increase in temperature occurring from February to April (an annual increase of 1.4–4.7 ºC in monthly mean, 0.6–4.0 ºC in monthly minimum, and 1.3–4.3 ºC in monthly maximum temperature) and the smallest in November and December. Temperature increases were lower in the tropics/subtropics (1.5–2.3 ºC from February–April) compared to the high-latitude areas (2.6–4.6 ºC from February–April). The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5 °C higher than those projected by GCMs. The estimated current Ae. albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China, with a risk period of June–September. The projected future Ae. albopictus risks in 2050 and 2080 cover nearly all of China, with an expanded risk period of April–October. The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.ConclusionsThe magnitude of climate change in China is likely to surpass GCM predictions. Future dengue risks will expand to cover nearly all of China if current climate trends continue.

Top-of-the-atmosphere and Vertical Cloud Structure of a Fast-rotating Late T Dwarf

Only a handful of late T brown dwarfs have been monitored for spectrophotometric variability, leaving incomplete the study of the atmospheric cloud structures of the coldest brown dwarfs, which share temperatures with some cold, directly imaged exoplanets. 2MASS J00501994–332240 is a T7.0 rapidly rotating, field brown dwarf that showed low-level photometric variability in data obtained with the Spitzer Space Telescope. We monitored 2MASS J00501994–332240 during ∼2.6 hr with MOSFIRE, installed at the Keck I telescope, with the aim of constraining its near-infrared spectrophotometric variability. We measured fluctuations with a peak-to-peak amplitude of 1.48% ± 0.75% in the J-band photometric light curve, an amplitude of 0.62% ± 0.18% in the J-band spectrophotometric light curve, an amplitude of 1.26% ± 0.93% in the H-band light curve, and an amplitude of 5.33% ± 2.02% in the CH4 − H2O band light curve. Nevertheless, the Bayesian information criterion does not detect significant variability in any of the light curves. Thus, given the detection limitations due to the MOSFIRE sensitivity, we can only claim tentative low-level variability for 2M0050–3322 in the best-case scenario. The amplitudes of the peak-to-peak fluctuations measured for 2MASS J00501994–332240 agree with the variability amplitude predictions of general circulation models for a T7.0 brown dwarf for an edge-on object. Radiative transfer models predict that the Na2S and KCl clouds condense at pressures lower than that traced by the CH4–H2O band, which might explain the higher peak-to-peak fluctuations measured for this light curve. Finally, we provide a visual recreation of the map provided by general circulation models and the vertical structure of 2MASS J00501994–332240 provided by radiative transfer models.

Thermal Phase Curves of XO-3b: An Eccentric Hot Jupiter at the Deuterium Burning Limit

Abstract We report Spitzer full-orbit phase observations of the eccentric hot Jupiter XO-3b at 3.6 and 4.5 μm. Our new eclipse depth measurements of 1770 ± 180 ppm at 3.6 μm and 1610 ± 70 ppm at 4.5 μm show no evidence of the previously reported dayside temperature inversion. We also empirically derive the mass and radius of XO-3b and its host star using Gaia DR3's parallax measurement and find a planetary mass M p = 11.79 ± 0.98 M Jup and radius R p = 1.295 ± 0.066 R Jup. We compare our Spitzer observations with multiple atmospheric models to constrain the radiative and advective properties of XO-3b. While the decorrelated 4.5 μm observations are pristine, the 3.6 μm phase curve remains polluted with detector systematics due to larger amplitude intrapixel sensitivity variations in this channel. We focus our analysis on the more reliable 4.5 μm phase curve and fit an energy balance model with solid body rotation to estimate the zonal wind speed and the pressure of the bottom of the mixed layer. Our energy balance model fit suggests an eastward equatorial wind speed of 3.13 − 0.83 + 0.26 km s−1, an atmospheric mixed layer down to 2.40 − 0.16 + 0.92 bars, and a Bond albedo of 0.106 − 0.106 + 0.008 . We assume that the wind speed and mixed layer depth are constant throughout the orbit. We compare our observations with 1D planet-averaged model predictions at apoapse and periapse and 3D general circulation model predictions for XO-3b. We also investigate the inflated radius of XO-3b and find that it would require an unusually large amount of internal heating to explain the observed planetary radius.

The Dark World: A Tale of WASP-43b in Reflected Light with HST WFC3/UVIS

Abstract Optical reflected light eclipse observations provide a direct probe of exoplanet scattering properties, such as from aerosols. We present here the photometric reflected light observations of WASP-43b using the Hubble Space Telescope (HST) WFC3/UVIS instrument with the F350LP filter (346–822 nm) encompassing the entire optical band. This is the first reflected light photometric eclipse using UVIS in scanning mode; as such, we further detail our scanning extraction and analysis pipeline Arctor. Our HST WFC3/UVIS eclipse light curve for WASP-43b derived a 3σ upper limit of 67 ppm on the eclipse depth, which implies that WASP-43b has a very dark dayside atmosphere. With our atmospheric modeling campaign, we compared our reflected light constraints with predictions from global circulation and cloud models benchmarked with HST and Spitzer observations of WASP-43b. We infer that we do not detect clouds on the dayside within the pressure levels probed by HST WFC3/UVIS with the F350LP filter (P > 1 bar). This is consistent with the general circulation model predictions based on previous WASP-43b observations. Dayside emission spectroscopy results from WASP-43b with HST and Spitzer observations are likely to not be significantly affected by contributions from cloud particles.

Impact of Systematic GCM Errors on Prediction Skill as Estimated by Linear Inverse Modeling

Statistical prediction of tropical sea surface temperatures (SSTs) is performed using linear inverse models (LIMs) that are constructed from both observations and general circulation model (GCM) output of SST. The goals are to establish a baseline for tropical SST predictions, to examine the extent to which the skill of a GCM-derived LIM is indicative of that GCM’s skill in forecast mode, and to examine the linkages between mean state bias and prediction skill. The observation-derived LIM is more skillful than a simple persistence forecasts in most regions. Its skill also compares well with some GCM forecasts except in the equatorial Pacific, where the GCMs are superior. The observation-derived LIM is matched or even outperformed by the GCM-derived LIMs, which may be related to the longer data record available for GCMs. The GCM-derived LIMs provide a fairly good measure for the skill achieved by their parent GCMs in forecast mode. In some cases, the skill of the LIM is actually superior to that of its parent GCM, indicating that the GCM predictions may suffer from initialization problems. A weak-to-moderate relation exists between model mean state error and prediction skill in some regions. An example is the eastern equatorial Atlantic, where an erroneously deep thermocline reduces SST variability, which in turn affects prediction skill. Another example is the equatorial Pacific, where skill appears to be linked to cold SST biases in the western tropical Pacific, which may reduce the strength of air–sea coupling.

Susceptibility of Hydropower Generation to Climate Change: Karun III Dam Case Study

Climate change can cause serious problems for future hydropower plant projects and make them less economically justified. Changing precipitation patterns, rising temperatures, and abrupt snow melting affect river stream patterns and hydropower generation. Thus, study of climate change impacts during the useful life of a hydropower dam is essential and its outcome should be considered in assessing long-term dam feasibility. The aim of this research is to evaluate the impacts of climate change on future hydropower generation in the Karun-III dam located in the southwest region of Iran in two future tri-decadal periods: near (2020–2049) and far (2070–2099). Had-CM3 general circulation model predictions under A2 and B2 SRES scenarios were applied, and downscaled by a statistical downscaling model (SDSM). An artificial neural network (ANN) and HEC-ResSim reservoir model respectively simulated the rainfall–runoff process and hydropower generation. The projections showed that the Karun-III dam catchment under the two scenarios will generally become warmer and wetter with a slightly larger increase in annual precipitation in the near than the far future. Runoff followed the precipitation trend by increasing in both periods. The runoff peak also switched from April to March in both scenarios, due to higher winter precipitation, and earlier snowmelt, which was caused by temperature rise. According to both scenarios, hydropower generation increased more in the near future than in the far future. Annual average power generation increased gradually by 26.7–40.5% under A2 and by 17.4–29.3% under B2 in 2020–2049. In the far period, average power generation increased by 1.8–8.7% in A2 and by 10.5–22% under B2. In the near future, A2 showed energy deduction in the months of June and July, while B2 revealed a decrease in the months of April and June. Additionally, projections in the 2070–2099 under A2 exhibited energy reduction in the months of March through July, while B2 revealed a decrease in April through July. The framework utilized in this study can be exploited to analyze the susceptibility of hydropower production in the long term.

Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network.

Based on the predictions of General Circulation Models, significant reduction of precipitation in Mediterranean areas is a possible scenario. Hence, better understanding of the spatial and temporal precipitation patterns is necessary in order to quantify desertification risks and design suitable mitigation measures. This study uses monthly precipitation measurements from a sparse network of 54 monitoring stations on the Mediterranean island of Crete (Greece). The study period extends from 1948 to 2012. The data reveal strong correlations between the western and eastern parts of the island. However, the average annual precipitation in the West is about 450 mm higher than that in the East. We construct a spatial model of average annual precipitation in Crete. The model involves a topographic trend and residuals with anisotropic spatial correlations which are fitted with a recently developed variogram function. We use regression kriging to generate annual precipitation maps and to identify locations of high estimation uncertainty. To our knowledge, this is the most detailed spatial analysis of precipitation in Crete to date. We present the analysis in detail for the year 1971. The trend accounts for ≈ 74% of the total variance. The highest precipitation estimate is 2331 mm in the West and 1781 mm in the East. The highest relative estimation uncertainty (≈ 20%) is observed along the southeastern coastline of the island, where the lowest values of annual precipitation are observed. This region includes one of the major agricultural areas of the island. The same overall patterns are found for other years in the study. Finally, we find no statistical evidence for a decrease in the global (over the entire island) annual precipitation during the study period.

Comparison of multiple downscaling techniques for climate change projections given the different climatic zones in China

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones.

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

AbstractSoil moisture‐air temperature coupling (SMTC) strength affects European summer air temperature variability. However, due to a lack of spatially extensive ground‐based soil moisture observations, General Circulation Model predictions of European‐mean SMTC strength have not been adequately verified and contain substantial uncertainties. Here we utilize remotely sensed soil moisture to evaluate estimates of SMTC strength provided by seven Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs). Soil moisture retrievals provided by the Soil Moisture Active/Passive (SMAP) mission are shown to be uniquely suited for this purpose. Comparisons to SMAP‐based estimates suggest that CMIP5 GCMs generally underestimate SMTC strengths—particularly in central Europe. This result is consistent with previous modeling work and highlights the value of L‐band soil moisture remote sensing for land surface‐atmosphere coupling analyses.

Enhancing the Predictability of Seasonal Streamflow With a Statistical‐Dynamical Approach

AbstractSeasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large‐scale climate indices as predictors in statistical models. In contrast, hybrid statistical‐dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon, and their skill is largely unknown. Here we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North American Multi‐Model Ensemble, 0.5–9.5 months ahead, at 290 stream gauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.

Thermospheric Expansion Associated With Dust Increase in the Lower Atmosphere on Mars Observed by MAVEN/NGIMS

AbstractWe present for the first time the dramatic variations in atmospheric composition and density at high altitudes from 170 to 220 km in Mars' neutral thermosphere in response to dust increases in the lower atmosphere, observed by the in situ Neutral Gas Ion Mass Spectrometer onboard the Mars Atmosphere and Volatile EvolutioN satellite. The observations reveal that CO2, Ar, N2, CO, and O densities all increase up to ~200% compared to the longer‐term running median densities. The density increases are seen throughout this altitude region, and the relative variations are seen to be stronger at higher altitudes. Density increases indicating the solar extreme ultraviolet influence are also seen. This study is consistent with that during increased dust load, the whole atmosphere expands and rises, and other processes may also be involved. The results agree with the general circulation model predictions, providing observational evidence for how dust increases affect different atmospheric species in the thermosphere.

Comparison of GCM Precipitation Predictions with Their RMSEs and Pattern Correlation Coefficients

This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures—the global, zonal, and grid mean—with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data.

Hydrologic impacts of changes in climate and glacier extent in the Gulf of Alaska watershed

AbstractHigh‐resolution regional‐scale hydrologic models were used to quantify the response of late 21st century runoff from the Gulf of Alaska (GOA) watershed to changes in regional climate and glacier extent. NCEP Climate Forecast System Reanalysis data were combined with five Coupled Model Intercomparison Project Phase 5 general circulation models (GCMs) for two representative concentration pathway (RCP) scenarios (4.5 and 8.5) to develop meteorological forcing for the period 2070–2099. A hypsographic model was used to estimate future glacier extent given assumed equilibrium line altitude (ELA) increases of 200 and 400 m. GCM predictions show an increase in annual precipitation of 12% for RCP 4.5 and 21% for RCP 8.5, and an increase in annual temperature of 2.5°C for RCP 4.5 and 4.3°C for RCP 8.5, averaged across the GOA. Scenarios with perturbed climate and glaciers predict annual GOA‐wide runoff to increase by 9% for RCP4.5/ELA200 case and 14% for the RCP8.5/ELA400 case. The glacier runoff decreased by 14% for RCP4.5/ELA200 and by 34% for the RCP8.5/ELA400 case. Intermodel variability in annual runoff was found to be approximately twice the variability in precipitation input. Additionally, there are significant changes in runoff partitioning and increases in snowpack runoff are dominated by increases in rain‐on‐snow events. We present results aggregated across the entire GOA and also for individual watersheds to illustrate the range in hydrologic regime changes and explore the sensitivities of these results by independently perturbing only climate forcings and only glacier cover.

Modelling Biome Shifts in the Indian Subcontinent under Scenarios of Future Climate Change

The Indian subcontinent is expected to witness signifi-cant changes in climatic conditions in the future, but the implications of such changes for future spatial dis-tribution of different biomes in the subcontinent are unclear. We sought to understand the potential shifts in the distribution of biomes in India by 2070 under different emission scenarios, identify biomes and re-gions of the country that are particularly at risk from future changes in climate, and quantify uncertainties associated with the predictions. We used an ensemble classifier (random forest) to model current and poten-tial future distribution of biomes in India for different climate trajectories under the newly developed repre-sentative concentration pathway (RCP) scenarios. Cli-mate projections from 19 and 17 different general circulation models (GCMs) were used to predict future biome distributions in India under the RCP 4.5 and RCP 8.5 scenarios respectively. For each scenario, mod-el outputs from different GCM projections were com-bined using a simple majority voting criterion. Approximately 630,000 sq. km (18%) of the country is predicted to experience biome shifts under the RCP 8.5 scenario and 486,000 sq. km (14%) under the RCP 4.5 scenario by 2070. Drier tropical biomes are likely to be replaced by wetter biomes, while temperate biomes are predicted to be dominated by vegetation characteristic of a warmer climate in the future. There was a high to moderate level of agreement between predictions of different GCMs. Our results suggest that biome shifts will be largely concentrated in the east-central and northern parts of the country, with tropical and sub-tropical dry forests, savannas, grasslands and xeric habitats particularly at risk. Future studies should focus on elucidating the responses of different vegetation sub-formations within individual biomes in order to gain a finer-scale understanding of vegetation responses to future climate change in the Indian subcontinent.

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

Seasonal variations in Titan’s middle atmosphere during the northern spring derived from Cassini/CIRS observations

We analyzed spectra acquired at the limb of Titan in the 2006–2013 period by the Cassini/Composite Infrared Spectrometer (CIRS) in order to monitor the seasonal evolution of the thermal, gas composition and aerosol spatial distributions. We are primarily interested here in the seasonal changes after the northern spring equinox and interpret our results in term of global circulation seasonal changes. Data cover the 600–1500cm−1 spectral range at a resolution of 0.5 or 15.5cm−1 and probe the 150–500km vertical range with a vertical resolution of about 30km. Retrievals of the limb spectra acquired at 15.5cm−1 resolution allowed us to derive eight global maps of temperature, aerosols and C2H2, C2H6 and HCN molecular mixing ratios between July 2009 and May 2013. In order to have a better understanding of the global changes taking place after the northern spring equinox, we analyzed 0.5cm−1 resolution limb spectra to infer the mixing ratio profiles of 10 molecules for some latitudes. These profiles are compared with CIRS observations performed during the northern winter. Our observations are compatible with the coexistence of two circulation cells upwelling at mid-latitudes and downwelling at both poles from at last January 2010 to at least June 2010. One year later, in June 2011, there are indications that the global circulation had reversed compared to the winter situation, with a single pole-to-pole cell upwelling at the north pole and downwelling at the south pole. Our observations show that in December 2011, this new pole-to-pole cell has settled with a downward velocity of 4.4mm/s at 450km above the south pole. Therefore, in about two years after the equinox, the global circulation observed during the northern winter has totally reversed, which is in agreement with the predictions of general circulation models. We observe a sudden unexpected temperature decrease above the south pole in February 2012, which is probably related to the strong enhancement of molecular gas in this region, acting as radiative coolers. In July and November 2012, we observe a detached haze layer located around 320–330km, which is comparable to the altitude of the detached haze layer observed by the Cassini Imaging Science Subsystem (ISS) in the UV.

How do soil and water conservation practices influence climate change impacts on potato production? Evidence from eastern Canada

We used a stochastic production function method together with a farm-level dataset covering 18 farms over a 23-year period to assess the role that soil and water conservation practices play in affecting the climate change impacts on potato yield in northwestern New Brunswick, Canada. Our analysis accounted for the yield effects of farm inputs, farm technologies, farm-specific factors, seasonal climatic variables, soil and water conservation practices, and a series of interaction terms between soil and water conservation practices and climatic variables. Regression results were used in combination with three climate change scenarios developed by the Intergovernmental Panel on Climate Change (A2, A1B, B1) and four general circulation model predictions over three 30-year time periods (2011–2040, 2041–2070, and 2071–2100) to estimate a range of potato yield projections over these time periods. Results show that accounting for soil and water conservation practices in climate–yield relationships increased the impacts of climate change on potato yield, with yield increases of up to 38 % by the 2071–2100 period. These findings provide evidence that adoption of soil and water conservation practices can help boost potato production in a changing Canadian climate.

Hydrological Cycles over the Congo and Upper Blue Nile Basins: Evaluation of General Circulation Model Simulations and Reanalysis Products

AbstractThe simulations and predictions of the hydrological cycle by general circulation models (GCMs) are characterized by a significant degree of uncertainty. This uncertainty is reflected in the range of Intergovernmental Panel on Climate Change (IPCC) GCM predictions of future changes in the hydrological cycle, particularly over major African basins. The confidence in GCM predictions can be increased by evaluating different GCMs, identifying those models that succeed in simulating the hydrological cycle under current climate conditions, and using them for climate change studies. Reanalyses are often used to validate GCMs, but they also suffer from an inaccurate representation of the hydrological cycle. In this study, the aim is to identify GCMs and reanalyses' products that provide a realistic representation of the hydrological cycle over the Congo and upper Blue Nile (UBN) basins. Atmospheric and soil water balance constraints are employed to evaluate the models' ability to reproduce the observed streamflow, which is the most accurate measurement of the hydrological cycle. Among the ECMWF Interim Re-Analysis (ERA-Interim), NCEP–NCAR reanalysis, and 40-yr ECWMF Re-Analysis (ERA-40), ERA-Interim shows the best performance over these basins: it balances the water budgets and accurately represents the seasonal cycle of the hydrological variables. The authors find that most GCMs used by the IPCC overestimate the hydrological cycle compared to observations. They observe some improvement in the simulated hydrological cycle with increased horizontal resolution, which suggests that some of the high-resolution GCMs are better suited for climate change studies over Africa.

Projected impacts of climate change on environmental suitability for malaria transmission in West Africa.

Background: Climate change is expected to affect the distribution of environmental suitability for malaria transmission by altering temperature and rainfall patterns; however, the local and global impacts of climate change on malaria transmission are uncertain.Objective: We assessed the effect of climate change on malaria transmission in West Africa.Methods: We coupled a detailed mechanistic hydrology and entomology model with climate projections from general circulation models (GCMs) to predict changes in vectorial capacity, an indication of the risk of human malaria infections, resulting from changes in the availability of mosquito breeding sites and temperature-dependent development rates. Because there is strong disagreement in climate predictions from different GCMs, we focused on the GCM projections that produced the best and worst conditions for malaria transmission in each zone of the study area.Results: Simulation-based estimates suggest that in the desert fringes of the Sahara, vectorial capacity would increase under the worst-case scenario, but not enough to sustain transmission. In the transitional zone of the Sahel, climate change is predicted to decrease vectorial capacity. In the wetter regions to the south, our estimates suggest an increase in vectorial capacity under all scenarios. However, because malaria is already highly endemic among human populations in these regions, we expect that changes in malaria incidence would be small.Conclusion: Our findings highlight the importance of rainfall in shaping the impact of climate change on malaria transmission in future climates. Even under the GCM predictions most conducive to malaria transmission, we do not expect to see a significant increase in malaria prevalence in this region.Citation: Yamana TK, Eltahir EA. 2013. Projected impacts of climate change on environmental suitability for malaria transmission in West Africa. Environ Health Perspect 121:1179–1186; http://dx.doi.org/10.1289/ehp.1206174

Evaluating explanatory models of the spatial pattern of surface climate trends using model selection and bayesian averaging methods

We evaluate three categories of variables for explaining the spatial pattern of warming and cooling trends over land: predictions of general circulation models (GCMs) in response to observed forcings; geographical factors like latitude and pressure; and socioeconomic influences on the land surface and data quality. Spatial autocorrelation (SAC) in the observed trend pattern is removed from the residuals by a well-specified explanatory model. Encompassing tests show that none of the three classes of variables account for the contributions of the other two, though 20 of 22 GCMs individually contribute either no significant explanatory power or yield a trend pattern negatively correlated with observations. Non-nested testing rejects the null hypothesis that socioeconomic variables have no explanatory power. We apply a Bayesian Model Averaging (BMA) method to search over all possible linear combinations of explanatory variables and generate posterior coefficient distributions robust to model selection. These results, confirmed by classical encompassing tests, indicate that the geographical variables plus three of the 22 GCMs and three socioeconomic variables provide all the explanatory power in the data set. We conclude that the most valid model of the spatial pattern of trends in land surface temperature records over 1979–2002 requires a combination of the processes represented in some GCMs and certain socioeconomic measures that capture data quality variations and changes to the land surface.