Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring

Building Climate Resilience in the Blue Nile/Abay Highlands: A Framework for Action

Ethiopia has become warmer over the past century and human induced climate change will bring further warming over the next century at unprecedented rates. On the average, climate models show a tendency for higher mean annual rainfall and for wetter conditions, in particular during October, November and December, but there is much uncertainty about the future amount, distribution, timing and intensity of rainfall. Ethiopia’s low level of economic development, combined with its heavy dependence on agriculture and high population growth rate make the country particularly susceptible to the adverse effects of climate change. Nearly 90% of Ethiopia’s population lives in the Highlands, which include the critical Blue Nile (Abay) Highlands—a region that holds special importance due to its role in domestic agricultural production and international water resources. A five year study of climate vulnerability and adaptation strategies in communities of Choke Mountain, located in the center of the Abay Highlands, has informed a proposed framework for enhancing climate resilience in communities across the region. The framework is motivated by the critical need to enhance capacity to cope with climate change and, subsequently, to advance a carbon neutral and climate resilient economy in Ethiopia. The implicit hypothesis in applying a research framework for this effort is that science-based information, generated through improved understanding of impacts and vulnerabilities of local communities, can contribute to enhanced resilience strategies. We view adaptation to climate change in a wider context of changes, including, among others, market conditions, the political-institutional framework, and population dynamics. From a livelihood perspective, culture, historical settings, the diversity of income generation strategies, knowledge, and education are important factors that contribute to adaptive capacities. This paper reviews key findings of the Choke Mountain study, describes the principles of the climate resilience framework, and proposes an implementation strategy for climate resilient development to be applied in the Abay Highlands, with potential expansion to agricultural communities across the region and beyond

Explaining National Trends in Terrestrial Water Storage

Access to fresh water is critical for human well-being, economic activity and, in some cases, political stability. Data from the Gravity Recovery and Climate Experiment (GRACE) has been used to monitor variability and trends in total water storage. This makes it possible to associate changes in water storage with both climate variability and large scale water management. Recent research has shown that these trends can be associated, globally, with rainfall, irrigation, and climate model predictions. This research indicates a need for further investigation into specific human predictors of trends in terrestrial water storage. This paper presents the first global scale analysis of GRACE trends focused on national scale socio-economic predictors of terrestrial water storage. We show that rainfall, irrigation, agricultural characteristics, and energy practices all contribute to GRACE trends, and the importance of each differs by country and region. Additionally, this work suggests that other factors such as GDP, population density, urbanization, and forest cover do not explain GRACE trends at a national level. Identifying these key predictors aids in understanding trends in water availability and for informing water management policy in a changing climate

Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July– September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented

Enhancing Dynamical Seasonal Predictions through Objective Regionalization

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July– September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology’s atmosphere–ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented

Impacts of anthropogenic heat on summertime rainfall in Beijing

Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL

Planning for compound hazards during the COVID-19 pandemic: The role of climate information systems

Roundtable on Compound Hazards and COVID-19 What: An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. When: 30 June 2021 Where: Online, convened by the World Meteorological Organization and hosted by the American Geophysical UnionPeer Reviewed"Article signat per 12 autors/es: Benjamin F. Zaitchik, Judy Omumbo, Rachel Lowe, Maarten van Aalst, Liana O. Anderson, Erich Fischer, Charlotte Norman, Joanne Robbins, Rosa Barciela, Juli Trtanj, Rosa von Borries, and Jürg Luterbacher"Postprint (published version

Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 19812014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist

Changing Patterns of Tree Cover in a Tropical Highland Region and Implications for Food, Energy, and Water Resources

The Blue Nile Highlands of Ethiopia are a densely populated, predominantly rural region dominated by smallholder crop-livestock mixed farming systems. Population growth, coupled with low productivity, have long posed a threat to natural forest ecosystems in the region, as trees have been removed for fuelwood and to clear area for grazing or crop production. In recent years, however, there has been a trend to replace cropland with eucalyptus plantations. This change has major implications for the hydrology, soils, and agricultural economy of the region. This study examines changes in tree cover for a highland area at the center of the Blue Nile Highlands. Landsat imagery from 1986 to 2017 is applied to characterize changing tree cover patterns over space and time. We find that total tree cover in this highland region has shifted dramatically over the past 30 years. Between 1987 and 1999 there was dramatic loss of tree cover, particularly in areas of natural vegetation at high and low elevation. This period coincided with the fall of the Derg government and the transition to the current political system. In the period since 1999 there has been an increase in tree cover, with rapid gains in recent years. This increase has taken two distinct forms: regrowth in previously forested areas, due in part to active conservation measures, and the establishment of eucalyptus plantations in mid-elevation zones. The ecological and economic implications of these two types of tree cover—protected forest vs. woodlot plantations—are quite distinct, with plantation forestry providing biomass energy at a cost to food production and water resources. Mapping cropland conversion to eucalyptus in recent years makes it possible to quantify the net impacts that this trend has had on local production of energy and food, and to estimate implications for water consumption. Effective monitoring of these changes is important for the ongoing development and implementation of effective land use policy in the region