Problems in estimating the response functions of natural climatic recording systems [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): High-resolution proxy records of climate, such as varves, ice cores, and tree-rings, provide the opportunity for reconstructing climate on a year-by-year basis. In order to do so it is necessary to approximate the complex nonlinear response function of the natural recording system using linear statistical models. Three problems with this approach were discussed, and possible solutions were suggested. Examples were given from a reconstruction of Santa Barbara precipitation based on tree-ring records from Santa Barbara County

400 years of precipitation variability in central and southern California reconstructed from tree-rings [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Precipitation variability at 31 stations hanging from San Diego to San Francisco and from the coast to the Sierras was characterized ..

Mapping Recent Decadal Climate Variations in Precipitation and Temperature across Eastern Africa and the Sahel

This chapter presents a novel interpolation approach that combines long-term mean satellite observations, station data, and topographic fields to produce grids of climate normals and trends. The approach was developed by the Climate Hazard Group (CHG) at the University of California, Santa Barbara (UCSB), to support food security analyses for the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET). The resulting FEWS NET Climatology (FCLIM) combines moving window regressions (MWRs) with geostatistical interpolation (kriging). Satellite and topographic fields often exhibit strong local correlations with in situ measurements of air temperature and rainfall. The FCLIM method uses these relationships to develop accurate and unbiased temperature and rainfall maps. The geostatistical estimation process provides standard error fields that take into account the density and spatial distribution of the point observations. These error fields are especially important when evaluating climate trends. Numerous climate change analyses present trend evaluations without assessing spatial uncertainty. In many of these studies, the number of recent observations can be very low, potentially invalidating the results. This study presents analyses for the Sahelian and eastern African rainfall and air temperatures. The results indicate significant rainfall declines in Sudan, Ethiopia, and Kenya. Every country exhibits significant increases in average air temperatures, with Sudan warming the most. This chapter concludes with a short discussion of how these results are being used to guide climate change adaptation, with a case study focused on Ethiopia

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Agricultural Drought Monitoring in Kenya Using Evapotranspiration Derived from Remote Sensing and Reanalysis Data

More than half of the people in sub-Saharan Africa live on less than US$ 1.25 per day, and nearly 30% do not receive sufficient nourishment to maintain daily health (UN, 2009a). These figures are expected to rise as a result of the recent global financial crisis that has led to an increase in food prices. Food for Peace (FFP), the program that administers more than 85% of U.S. international food aid, recently reported that the seven largest recipient countries of food aid worldwide are in sub-Saharan Africa (FFP, 2010). In Kenya, the fifth largest recipient of food aid from FFP and a country highly dependent on rainfed agriculture, below-average precipitation in 2009 led to a 20% reduction in maize production and a 100% increase in domestic maize prices (FEWS NET, 2009). Given these sorts of climatic shocks, it is imperative that mitigation strategies be developed for sub-Saharan Africa and other regions of the developing world to improve the international and national response to impending food crises. Crop monitoring is an important tool used by national agricultural offices and other stakeholders to inform food security analyses and agricultural drought mitigation. Remote sensing and surface reanalysis data facilitate efficient and cost-effective approaches to measuring determinants of agricultural drought. In this chapter, we explore how remotely sensed estimates of actual evapotranspiration (ETa) can be integrated with surface reanalysis data to augment agricultural drought monitoring systems. Although water availability is important throughout every stage of crop development, from germination to harvest, crops are most sensitive to moisture deficits during the reproductive stages (Shanahan and Nielsen, 1987). A study that analyzed maize, for example, showed that a 1% decline in seasonal ETa led to an average loss of 1.5% in crop yield, whereas water stress in the same proportion concentrated during the reproductive phases led to a 2.6% decline in crop yield (Stegman, 1982). Agricultural drought can therefore be defined as inadequate soil water availability, particularly during the reproductive phase, caused by low precipitation, insufficient water-holding capacity in the root zone of the soil, and/or high atmospheric water demand (potential evapotranspiration, ETp), which results in a reduction in crop yield. Agricultural droughts differ in timescale and impact from shorter-term meteorological droughts, which are characterized by negative precipitation anomalies on the order of days to weeks, and the longer-term negative runoff and water storage anomalies that characterize hydrological drought (Dracup et al., 1980)

Best practices for crop area estimation with Remote Sensing

This document gives recommendations on different ways to use Earth Observation as a tool for crop area estimation highlighting strengths and limitations. The document was discussed in a workshop hosted by the Joint Research Centre (JRC) of the European Commission in June 2008 and attended by 40 remote sensing practitioners and users.JRC.DG.G.3-Monitoring agricultural resource

Using Tree Rings to Predict the Response of Tree Growth to Climate Change in the Continental United States during the Twenty-First Century

In the early 1900s, tree-ring scientists began analyzing the relative widths of annual growth rings preserved in the cross sections of trees to infer past climate variations. Now, many ring-width index (RWI) chronologies, each representing a specific site and species, are archived online within the International Tree-Ring Data Bank (ITRDB). Comparing annual tree-ring-width data from 1097 sites in the continental United States to climate data, the authors quantitatively evaluated how trees at each site have historically responded to interannual climate variations. For each site, they developed a climate-driven statistical growth equation that uses regional climate variables to model RWI values. The authors applied these growth models to predict how tree growth will respond to twenty-first-century climate change, considering four climate projections. Although caution should be taken when extrapolating past relationships with climate into the future, the authors observed several clear and interesting patterns in the growth projections that seem likely if warming continues. Most notably, the models project that productivity of dominant tree species in the southwestern United States will decrease substantially during this century, especially in warmer and drier areas. In the northwest, nonlinear growth relationships with temperature may lead to warming-induced declines in growth for many trees that historically responded positively to warmer temperatures. This work takes advantage of the unmatched temporal length and spatial breath of annual growth data available within the ITRDB and exemplifies the potential of this ever-growing archive of tree-ring data to serve in meta-analyses of large-scale forest ecology

High-resolution natural recording systems: intercomparisons of the response to interannual climatic variability [abstract]

The goal of this work is to examine the properties of recording mechanisms which are common to continuously recording high-resolution natural systems in which climatic signals are imprinted and preserved as proxy records. These systems produce seasonal structures as an indirect response to climatic variability over the annual cycle. We compare the proxy records from four different high-resolution systems: the Quelccaya ice cap of the Peruvian Andes; composite tree ring growth from southern California and the southwestern United States; and the marine varve sedimentation systems in the Santa Barbara basin (off California, United States) and in the Gulf of California, Mexico. An important focus of this work is to indicate how the interannual climatic signal is recorded in a variety of different natural systems with vastly different recording mechanisms and widely separated in space. These high-resolution records are the products of natural processes which should be comparable, to some degree, to human-engineered systems developed to transmit and record physical quantities. We therefore present a simple analogy of a data recording system as a heuristic model to provide some unifying concepts with which we may better understand the formation of the records. This analogy assumes special significance when we consider that natural proxy records are the principal means to extend our knowledge of climatic variability into the past, beyond the limits of instrumentally recorded data

Warming of the Indian Ocean Threatens Eastern and Southern Africa, but could be Mitigated by Agricultural Development

Since 1980, the number of undernourished people in eastern and southern Africa has more than doubled. Rural development stalled and rural poverty expanded during the 1990s. Population growth remains very high and declining per capita agricultural capacity retards progress towards Millennium Development goals. Analyses of in situ station data and satellite observations of precipitation identify another problematic trend. Main growing season rainfall receipts have diminished by approximately 15% in food insecure countries clustered along the western rim of the Indian Ocean. Occurring during the main growing seasons in poor countries dependent on rain fed agriculture, these declines are societally dangerous. Will they persist or intensify? Tracing moisture deficits upstream to an anthropogenically warming Indian Ocean leads us to conclude that further rainfall declines are likely. We present analyses suggesting that warming in the central Indian Ocean disrupts onshore moisture transports, reducing continental rainfall. Thus late 20th century anthropogenic Indian Ocean warming has probably already produced societally dangerous climate change by creating drought and social disruption in some of the world's most fragile food economies. We quantify the potential impacts of the observed precipitation and agricultural capacity trends by modeling millions of undernourished people as a function of rainfall, population, cultivated area, seed and fertilizer use. Persistence of current tendencies may result in a 50% increase in undernourished people. On the other hand, modest increases in per capita agricultural productivity could more than offset the observed precipitation declines. Investing in agricultural development can help mitigate climate change while decreasing rural poverty and vulnerability

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2