Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 18deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain

Potential impact of climate change on cereal crop yield in West Africa

PRIFPRI3; HarvestChoice; ISI; CRP2EPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

Projecting regional climate and cropland changes using a linked biogeophysical-socioeconomic modeling framework: 2. Transient dynamics

PRIFPRI3; ISI; CRP2; Capacity Strengthening; A Ensuring Sustainable food productionEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

Projecting regional climate and cropland changes using a linked biogeophysical‐socioeconomic modeling framework: 2. Transient dynamics

Abstract Understanding climate‐cropland interactions and their impact on future projections in West Africa motivated the recent development of a modeling framework that asynchronously couples four models for regional climate, crop growth, socioeconomics, and cropland allocation. This modeling framework can be applied to a future time slice using an equilibrium approach or to a continuous projection using a transient approach. This paper compares the differences between these two approaches, examines the transient dynamics of the system, and evaluates its impact on future projections. During the course of projection up to mid‐century, food demand is projected to increase monotonically, while the projected crop yield shows a high degree of temporal dynamics due to strong climate variability. Such temporal dynamics are not accounted for by the equilibrium approach. As a result, the transient approach projects a generally faster future expansion of cropland, with the largest differences over Benin, Burkina Faso, Ghana, Senegal, and Togo. Despite the relative large differences between the two approaches in projecting land cover changes associated with cropland expansion, the projected future climate changes are fairly similar. While the additional cropland expansion in the transient approach favors a wet signal, both the transient and equilibrium approaches project a future decrease of rainfall in the western part of West Africa and an increase in the eastern part. For quantifying climate changes, the equilibrium application of the modeling framework is likely to be sufficient; for assessing climate impact on agricultural sectors and devising mitigation and adaptation strategies, transient dynamics is important

Projecting regional climate and cropland changes using a linked biogeophysical‐socioeconomic modeling framework: 1. Model description and an equilibrium application over West Africa

Agricultural land use alters regional climate through modifying the surface mass, energy, and momentum fluxes; climate influences agricultural land use through their impact on crop yields. These interactions are not well understood and have not been adequately considered in climate projections. This study tackles the critical linkages within the coupled natural-human system of West Africa in a changing climate based on an equilibrium application of a modeling framework that asynchronously couples models of regional climate, crop yield, multimarket agricultural economics, and cropland expansion. Using this regional modeling framework driven with two global climate models, we assess the contributions of land use change (LUC) and greenhouse gas (GHGs) concentration changes to regional climate changes and assess the contribution of climate change and socioeconomic factors to agricultural land use changes. For future cropland expansion in West Africa, our results suggest that socioeconomic development would be the dominant driver in the east (where current cropland coverage is already high) and climate changes would be the primary driver in the west (where future yield drop is severe). For future climate, it is found that agricultural expansion would cause a dry signal in the west and a wet signal in the east downwind, with an east-west contrast similar to the GHG-induced changes. Over a substantial portion of West Africa, the strength of the LUC-induced climate signals is comparable to the GHG-induced changes. Uncertainties originating from the driving global models are small; human decision making related to land use and international trade is a major source of uncertainty