Multi-factor impact analysis of agricultural production in Bangladesh with climate change

Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040–2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21 Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins’ hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.     

High resolution modeling of the regional impacts of climate change on irrigation water demand

In the Arkansas River Basin in southeastern Colorado, surface irrigation provides most of the water required for agriculture. Consequently, the region’s future could be significantly affected if climate change impacts the amount of water available for irrigation. A methodology to model the expected impacts of climate change on irrigation water demand in the region is described. The Integrated Decision Support Consumptive Use model, which accounts for spatial and temporal variability in evapotranspiration and precipitation, is used in conjunction with two climate scenarios from the Vegetation-Ecosystem Modeling and Analysis Project. The two scenarios were extracted and scaled down from two general circulation models (GCMs), the HAD from the Hadley Centre for Climate Prediction and Research and the CCC from the Canadian Climate Centre. The results show significant changes in the water demands of crops due to climate change. The HAD and CCC climate change scenarios both predict an increase in water demand. However, the projections of the two GCMs concerning the water available for irrigation differ significantly, reflecting the large degree of uncertainty concerning what the future impacts of climate change might be in the study region. As new or updated predictions become available, the methodology described here can be used to estimate the impacts of climate change.     

Use of a reservoir water quality model to simulate global climate change effects on fish habitat

A case study was conducted on the potential impacts of climate change on fish habitat in a southeastern reservoir. A reservoir water quality model and one year of baseline meteorologic, hydrologic, and inflow water quality input were used to simulate current reservoir water quality. Total adult striped bass habitat, defined by specific quantitative temperature and dissolved oxygen criteria, was simulated. Daily reservoir volumes with optimal, suboptimal, and unsuitable temperature and DO were predicted for the year. Output from recent runs of atmospheric general circulation models (GCMs), in which atmospheric carbon dioxide concentrations have been doubled, was then used to adjust the baseline inputs to the water quality model. New sets of input data were created for two grid cells for each of three GCMs. All six climate scenarios are predicted to cause overall declines in the available summer striped bass habitat, mostly due to lake water temperatures exceeding striped bass tolerance levels. These predictions are believed to result from the consensus among GCM scenarios that air temperatures and humidity will rise, and the sensitivity of the reservoir model to these parameters. The reservoir model was found to be a promising tool for examining potential climate-change impacts. Some of the assumptions required to apply GCM output to the reservoir model, however, illustrate the problems in using large-scale gridcell output to assess small-scale impacts.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Climate Change Impacts for the Conterminous USA: An Integrated Assessment Summary

This special issue of Climatic Change describes an effort to improve methodology for integrated assessment of impacts and consequences of climatic change. Highlights of the seven foregoing Parts (papers) that constitute this special issue are summarized here. The methodology developed involves construction of scenarios of climate change that are used to drive individual sectoral models for simulating impacts on crop production, irrigation demand, water supply and change in productivity and geography of unmanaged ecosystems. Economic impacts of the changes predicted by integrating the results of the several sectoral simulation models are calculated through an agricultural land-use model. While these analyses were conducted for the conterminous United States alone, their global implications are also considered in this summary as is the need for further improvements in integrated assessment methodology.     

Laurentian Great Lakes double-CO2 climate change hydrological impacts

The Great Lakes Environmental Research Laboratory has developed conceptual daily models for simulating moisture storages in and runoff from the 121 watersheds draining into the Laurentian Great Lakes, over-lake precipitation into each lake, and the heat storages in and evaporation from each lake. We combine these components as net basin supplies for each lake to consider climate change scenarios developed from atmospheric general circulation models (GCMs). Recent scenarios of a doubling of atmospheric Co₂, available from the Goddard Institute for Space Studies, the Geophysical Fluid Dynamics Laboratory, and Oregon State University are considered by making changes in historical meteorological data similar to the changes observed in the GCMs, observing the impact of the changed data in the model outputs, and comparing outputs to model results using unchanged data, representing comparison to an unchanged atmosphere. This study indicates a 23 to 51% reduction in net basin supplies to all the Great Lakes; there is significant variation in the components of these supplies among the three GCMs. The basins various moisture storages become dryer and the lakes are warmer with associated hydrological impacts.GLERL Contribution NO. 646.     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

GCM Simulations of Stable Isotopes in the Water Cycle in Comparison with GNIP Observations over East Asia

In this paper, we examine the performance of four isotope incorporated GCMs, i.e., ECHAM4 (University of Hamburg), HadCM3 (Hadley Centre), GISS E (Goddard Institute of Space Sciences), and MUGCM (Melbourne University), by comparing the model results with GNIP (Global Network of Isotopes in Precipitation) observations. The spatial distributions of mean annual δD and mean annual deuterium excess d in precipitation, and the relationship between δ18 o and δD in precipitation, are compared between GCMs and GNIP data over East Asia. Overall, the four GCMs reproduce major characteristics of δD in precipitation as observed by GNIP. Among the four models, the results of ECHAM4 and GISS E are more consistent with GNIP observed precipitation δD distribution. The simulated d distributions are less consistent with the GNIP results. This may indicate that kinetic fractionation processes are not appropriately represented in the isotopic schemes of GCMs. The GCM modeled MWL (meteoric water line) slopes are close to the GNIP derived MWL, but the simulated MWL intercepts are significantly overestimated. This supports that the four isotope incorporated GCMs may not represent the kinetic fractionation processes well. In term of LMWLs (local meteoric water lines), the simulated LMWL slopes are similar to those from GNIP observations, but slightly overestimated for most locations. Overall, ECHAM4 has better capability in simulating MWL and LMWLs, followed by GISS E. Some isotopic functions (especially those related to kinetic fractionation) and their parameterizations in GCMs may have caused the discrepancy between the simulated and GNIP observed results. Future work is recommended to improve isotopic function parameterization on the basis of the high-resolution isotope observations.     

Evaluating GCM Output with Impact Models

This study uses empirical agricultural impact models to compare the U.S. climate change predictions of 16 General Circulation Models (GCMs). The impact analysis provides a policy-relevant index by which to judge complex climate predictions. National aggregate impacts vary widely across the 16 GCMs because of varying regional and seasonal patterns of predicted climate change. Examining the predicted impacts from the full set of GCMs reveals that the seasonal detail in the GCM predictions is so noisy that it is not significantly different from a constant annual change. However, a consistent regional pattern does emerge across the set of models. Nonetheless, aggregating climate change across seasons and regions within the United States, using a national-annual climate change provides a reasonable and efficient approximation to the expected impact predicted by the 16 GCM models.     

The integration of PESETA sectoral economic impacts into the GEM-E3 Europe model: methodology and results

The PESETA project has estimated the physical effects of climate change in Europe for the following impact categories with a market valuation: agriculture, river floods, coastal systems and tourism. Four alternative scenarios of future climate change have been considered. The computable general equilibrium (CGE) GEM-E3 model for Europe has been used to integrate the PESETA damages under a consistent economic framework. The approach followed has been to assess the effects of future climate (as of 2080s) on today’s economy. This article details the way each sectoral impact has been integrated into the CGE model. The EU welfare loss is estimated to be in a range of 0.2% to 1%, depending on the climate future and the projected sea level rise. Results show that the Southern Europe region appears as the most vulnerable area to climate change. Impacts in coastal systems, agriculture and river floods determine the overall and regional pattern of impacts within Europe.     

Assessing climate change impacts on hydrology from an ensemble of regional climate models,model scales and linking methods – a case study on the Lule River basin

This paper investigates how using different regional climate model (RCM) simulations affects climate change impacts on hydrology in northern Europe using an offline hydrological model. Climate change scenarios from an ensemble of seven RCMs, two global climate models (GCMs), two global emissions scenarios and two RCMs of varying resolution were used. A total of 15 climate change simulations were included in studies on the Lule River basin in Northern Sweden. Two different approaches to transfer climate change from the RCMs to hydrological models were tested. A rudimentary estimate of change in hydropower potential on the Lule River due to climate change was also made. The results indicate an overall increase in river flow, earlier spring peak flows and an increase in hydropower potential. The two approaches for transferring the signal of climate change to the hydrological impacts model gave similar mean results, but considerably different seasonal dynamics, a result that is highly relevant for other types of climate change impacts studies.     

Hydrologic impacts of climate change on the Nile River Basin: implications of the 2007 IPCC scenarios

We assess the potential impacts of climate change on the hydrology and water resources of the Nile River basin using a macroscale hydrology model. Model inputs are bias corrected and spatially downscaled 21st Century simulations from 11 General Circulation Models (GCMs) and two global emissions scenarios (A2 and B1) archived from the 2007 IPCC Fourth Assessment Report (AR4). While all GCMs agree with respect to the direction of 21st Century temperature changes, there is considerable variability in the magnitude, direction, and seasonality of projected precipitation changes. Our simulations show that, averaged over all 11 GCMs, the Nile River is expected to experience increase in streamflow early in the study period (2010–2039), due to generally increased precipitation. Streamflow is expected to decline during mid- (2040–2069) and late (2070–2099) century as a result of both precipitation declines and increased evaporative demand. The predicted multimodel average streamflow at High Aswan Dam (HAD) as a percentage of historical (1950–1999) annual average are 111 (114), 92 (93) and 84 (87) for A2 (B1) global emissions scenarios. Implications of these streamflow changes on the water resources of the Nile River basin were analyzed by quantifying the annual hydropower production and irrigation water release at HAD. The long-term HAD release for irrigation increases early in the century to 106 (109)% of historical, and then decreases to 87 (89) and 86 (84)% of historical in Periods II and III, respectively, for the A2 (B1) global emissions scenarios. Egypt’s hydropower production from HAD will be above the mean annual average historical value of about 10,000 GWH for the early part of 21st century, and thereafter will generally follow the streamflow trend, however with large variability among GCMs. Agricultural water supplies will be negatively impacted, especially in the second half of the century.     

Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5)

Following the CORDEX experimental protocol, climate simulations and climate-change projections for Africa were made with the new fifth-generation Canadian Regional Climate Model (CRCM5). The model was driven by two Global Climate Models (GCMs), one developed by the Max-Planck-Institut für Meteorologie and the other by the Canadian Centre for Climate Modelling and Analysis, for the period 1950–2100 under the RCP4.5 emission scenario. The performance of the CRCM5 simulations for current climate is discussed first and compared also with a reanalysis-driven CRCM5 simulation. It is shown that errors in lateral boundary conditions and sea-surface temperature from the GCMs have deleterious consequences on the skill of the CRCM5 at reproducing specific regional climate features such as the West African Monsoon and the annual cycle of precipitation. For other aspects of the African climate however the regional model is able to add value compared to the simulations of the driving GCMs. Climate-change projections for periods until the end of this century are also analysed. All models project a warming throughout the twenty-first century, although the details of the climate changes differ notably between model projections, especially for precipitation changes. It is shown that the climate changes projected by CRCM5 often differ noticeably from those of the driving GCMs.     

The future of the CDM: same same,but differentiated?

Policy-makers and scientists have raised concerns about the functioning of the Clean Development Mechanism (CDM), in particular regarding its low contribution to sustainable development, unbalanced regional and sectoral distribution of projects, and its limited contribution to global emission reductions. Differentiation between countries or project types has been proposed as a possible way forward to address these problems. An overview is provided of the different ways in which CDM differentiation could be implemented. The implications for the actors involved in the CDM are analysed, along with a quantitative assessment of the impacts on the carbon market, using bottom-up marginal abatement cost curves. The discounting of CDM credits, quota systems, or differentiated eligibility of countries could help to address several of the concerns raised. Preferential treatment may also make a limited contribution to achieving the aims of CDM differentiation by increasing opportunities for under-represented host countries. The impact on the carbon market appears to be limited for most options.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

海气耦合对MJO数值模拟的影响

采用一种基于降水异常追踪MJO（Madden–Julian Oscillation）东传的MJO识别方法（MJO Tracking）评估了参与MJOTF/GASS（MJO Task Force/Global Energy and Water Cycle Experiment Atmospheric System Study）全球模式比较计划的全海气耦合模式（CNRM-CM）、半海气耦合模式（CNRM-ACM）和大气模式（CNRM-AM）1991～2010年模拟MJO的能力,探究了海气耦合过程对模式模拟MJO能力的影响机理。CNRM-CM模式模拟的MJO结构更加接近观测,该模式不仅具有最高的MJO生成频率,也能够模拟较强的MJO强度以及较远的传播距离。海气耦合过程会造成CNRM-CM和CNRM-ACM模式中印度洋—太平洋暖池区域海温气候态的冷偏差。但是这种海温气候态的偏差基本没有改变模式模拟MJO的能力。CNRM-CM中MJO对流中心东（西）侧存在较强的季节内尺度海温暖（冷）异常,纬向梯度明显,而CNRM-ACM和CNRM-AM中不存在这样的海温东西不对称结构。结果表明在CNRM模式中海气耦合过程调控模式海温季节内尺度变率对模式MJO模拟能力的影响比调控模式海温气候态更加重要。     

A quantitative performance assessment of cloud regimes in climate models

Differences in the radiative feedback from clouds account for much of the variation in climate sensitivity amongst General Circulation Models (GCMs). Therefore metrics of model performance which are demonstrated to be relevant to the cloud response to climate change form an important contribution to the overall evaluation of GCMs. In this paper we demonstrate an alternative method for assigning model data to observed cloud regimes obtained from clustering histograms of cloud amount in joint cloud optical depth—cloud top pressure classes. The method removes some of the subjectivity that exists in previous GCM cloud clustering studies. We apply the method to ten GCMs submitted to the Cloud Feedback Model Intercomparison Project (CFMIP), evaluate the simulated cloud regimes and analyse the climate change response in the context of these regimes. We also propose two cloud regime metrics, one of which is specifically targeted at assessing GCMs for the purpose of obtaining the global cloud radiative response to climate change. Most of the global variance in the cloud radiative response between GCMs is due to low clouds, with 47% arising from the stratocumulus regime and 18% due to the regime characterised by clouds undergoing transition from stratocumulus to cumulus. This result is found to be dominated by two structurally similar GCMs. The shallow cumulus regime, though widespread, has a smaller contribution and reduces the variance. For the stratocumulus and transition regimes, part of the variance results from a large model spread in the radiative properties of the regime in the control simulation. Comparison with observations reveals a systematic bias for both the stratocumulus and transition regimes to be overly reflective. If this bias was corrected with all other aspects of the response unchanged, the variance in the low cloud response would reduce. The response of some regimes with high cloud tops differ between the GCMs. These regimes are simulated too infrequently in a few of the models. If the frequency in the control simulation were more realistic and changes within the regimes were unaltered, the variance in the cloud radiative response from high-top clouds would increase. As a result, use of observations of the mean present-day cloud regimes suggests that whilst improvements in the simulation of the cloud regimes would impact the climate sensitivity, the inter-model variance may not reduce. When the cloud regime metric is calculated for the GCMs analysed here, only one model is on average consistent with observations within their uncertainty (and even this model is not consistent with the observations for all regimes), indicating scope for improvement in the simulation of cloud regimes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Assessing the site-specific impacts of climate change on hydrology, soil erosion and crop yields in the Loess Plateau of China

Climate changes may have great impacts on the fragile agro-ecosystems of the Loess Plateau of China, which is one of the most severely eroded regions in the world. We assessed the site-specific impacts of climate change during 2010?C2039 on hydrology, soil loss and crop yields in Changwu tableland region in the Loess Plateau of China. Projections of four climate models (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) under three emission scenarios (A2, B2 and GGa) were used. A simple spatiotemporal statistical method was used to downscale GCMs monthly grid outputs to station daily weather series. The WEPP (Water and Erosion Prediction Project) model was employed to simulate the responses of agro-ecosystems. Compared with the present climate, GCMs projected a ?2.6 to 17.4% change for precipitation, 0.6 to 2.6°C and 0.6 to 1.7°C rises for maximum and minimum temperature, respectively. Under conventional tillage, WEPP predicted a change of 10 to 130% for runoff, ?5 to 195% for soil loss, ?17 to 25% for wheat yield, ?2 to 39% for maize yield, ?14 to 18% for plant transpiration, ?8 to 13% for soil evaporation, and ?6 to 9% for soil water reserve at two slopes during 2010?C2039. However, compared with conventional tillage under the present climate, conservation tillage would change runoff by ?34 to 71%, and decrease soil loss by 26 to 77% during 2010?C2039, with other output variables being affected slightly. Overall, climate change would have significant impacts on agro-ecosystems, and adoption of conservation tillage has great potential to reduce the adverse effects of future climate changes on runoff and soil loss in this region.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: baseline climate

Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows.