Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation - May 2012

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.44°) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only, ~15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (~47% under RCP4.5, ~49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas.

The severe impacts of climate variability and climate hazards on society reveal the increasing need for improving regional- and local-scale climate diagnosis. A new downscaling approach for climate diagnosis is presented here. It is based on artificial neural network (ANN) techniques that derive relationships from the large- and local-scale atmospheric controls to the local winter climate. This study documents the large-scale conditions associated with extreme precipitation events in northeastern Mexico and southeastern Texas during the 1985–93 period, and demonstrates the ability of ANN to simulate realistic relationships between circulation–humidity fields and daily precipitation at local scale. The diagnostic model employs a neural network that preclassifies the winter circulation and humidity fields into different patterns. The results from this neural network classification approach, known as a self-organizing map (SOM), indicate that negative (positive) anomalies of winter precipitation ove... Abstract The severe impacts of climate variability and climate hazards on society reveal the increasing need for improving regional- and local-scale climate diagnosis. A new downscaling approach for climate diagnosis is presented here. It is based on artificial neural network (ANN) techniques that derive relationships from the large- and local-scale atmospheric controls to the local winter climate. This study documents the large-scale conditions associated with extreme precipitation events in northeastern Mexico and southeastern Texas during the 1985–93 period, and demonstrates the ability of ANN to simulate realistic relationships between circulation–humidity fields and daily precipitation at local scale. The diagnostic model employs a neural network that preclassifies the winter circulation and humidity fields into different patterns. The results from this neural network classification approach, known as a self-organizing map (SOM), indicate that negative (positive) anomalies of winter precipitation ove...

The role of the Southern Oscillation (SO) in the interannual variability of convection and rainfall over Mexico is studied by correlation analysis of long‐term precipitation records, a satellite‐derived set of highly reflective clouds (HRC), upper‐air soundings, surface ship observations, and an SO index defined as high for anomalously high low pressure at Tahiti/Darwin.Highly reflective clouds and rainfall have a similar annual cycle and interannual variability, except in certain regions and seasons with prevalence of stratiform cloudiness. During the boreal winter (November‐April) dry season, the low SO phase (or El NiñMo) is, in much of the country, characterized by increased precipitation associated with the enhanced influence of mid‐latitude westerlies. However, in the Isthmus of Tehuantepec, most exposed to Norte invasions, precipitation is larger in the high SO phase. During the boreal summer (May‐October) rainy season, precipitation is more abundant in the high SO phase, when the northward retraction of the North Atlantic High, weaker trades, and a northward displaced near‐equatorial trough are conducive to enhanced ascending motion over the area.

The intraseasonal evolution of the North American monsoon in southeast Arizona during the 1980–93 period is investigated using a neural network–based nonlinear classification technique known as the self-organizing map (SOM). The goal of the SOM algorithm is to discover meaningful low-dimensional structures hidden in the high-dimensional observations. Various daily lagged atmospheric fields (850-hPa meridional winds, 700-hPa specific humidity, 500-hPa geopotential heights, and 850–500-hPa thickness) for the summer season (June–July–August–September) of the 1980–93 period are used in the nonlinear classification of monsoon modes. Special emphasis is given to the wettest monsoon modes. The neural network classification successfully captures the multidimensional interaction of the atmospheric variables during the monsoon evolution, and shows monsoon “bursts” and “breaks” in a given year. Spectral analysis of daily summer rainfall in the study area reveals a significant peak in the 12–18-day band; a s... Abstract The intraseasonal evolution of the North American monsoon in southeast Arizona during the 1980–93 period is investigated using a neural network–based nonlinear classification technique known as the self-organizing map (SOM). The goal of the SOM algorithm is to discover meaningful low-dimensional structures hidden in the high-dimensional observations. Various daily lagged atmospheric fields (850-hPa meridional winds, 700-hPa specific humidity, 500-hPa geopotential heights, and 850–500-hPa thickness) for the summer season (June–July–August–September) of the 1980–93 period are used in the nonlinear classification of monsoon modes. Special emphasis is given to the wettest monsoon modes. The neural network classification successfully captures the multidimensional interaction of the atmospheric variables during the monsoon evolution, and shows monsoon “bursts” and “breaks” in a given year. Spectral analysis of daily summer rainfall in the study area reveals a significant peak in the 12–18-day band; a s...