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Joel Michaelsen

Publications and source records attributed to Joel Michaelsen.

7 recordsLinked to original sources
USGS2019

A high-resolution 1983-2016 Tmax climate data record based on InfraRed Temperatures and Stations by the Climate Hazard Center

Understanding the dynamics and physics of climate extremes will be a critical challenge for 21st century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016 the number of observations in the CRU Tmax product declined precipitously (5,900→1,000); 1,000 poorly distributed measurements are not enough to resolve regional Tmax variations. Here we show that long (1983-near present) high resolution (0.05°), cloud-screened archives of geostationary satellite Thermal Infrared Red (TIR) observations, when combined with a dense set of ~15,000 station observations, can explain 23, 40, 30, 11% more variance than the CRU over the globe, South America, Africa, India and areas north of 50°N, with even greater levels of improvement over the 2011-2016 period (28, 45, 39, 52, 28%). Described here for the first time, the TIR Tmax algorithm uses sub-daily TIR distributions to screen out cloud contaminated observations, providing accurate (correlation≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15,000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicates that the satellite-only, station-only and combined products all perform accurately (R≈0.8-0.9, mean absolute errors ≈0.8-1.0). Hence, the Climate Hazards center InfraRed Temperature with Stations (CHIRTSmax) data set should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications.

Journal of Climate↗
USGS2015

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Scientific Data↗
USGS2011

Recent summer precipitation trends in the Greater Horn of Africa and the emerging role of Indian Ocean sea surface temperature

We utilize a variety of climate datasets to examine impacts of two mechanisms on precipitation in the Greater Horn of Africa (GHA) during northern-hemisphere summer. First, surface-pressure gradients draw moist air toward the GHA from the tropical Atlantic Ocean and Congo Basin. Variability of the strength of these gradients strongly influences GHA precipitation totals and accounts for important phenomena such as the 1960s–1980s rainfall decline and devastating 1984 drought. Following the 1980s, precipitation variability became increasingly influenced by the southern tropical Indian Ocean (STIO) region. Within this region, increases in sea-surface temperature, evaporation, and precipitation are linked with increased exports of dry mid-tropospheric air from the STIO region toward the GHA. Convergence of dry air above the GHA reduces local convection and precipitation. It also produces a clockwise circulation response near the ground that reduces moisture transports from the Congo Basin. Because precipitation originating in the Congo Basin has a unique isotopic signature, records of moisture transports from the Congo Basin may be preserved in the isotopic composition of annual tree rings in the Ethiopian Highlands. A negative trend in tree-ring oxygen-18 during the past half century suggests a decline in the proportion of precipitation originating from the Congo Basin. This trend may not be part of a natural cycle that will soon rebound because climate models characterize Indian Ocean warming as a principal signature of greenhouse-gas induced climate change. We therefore expect surface warming in the STIO region to continue to negatively impact GHA precipitation during northern-hemisphere summer.

Greater Horn of Africa↗
USGS2011

The Forecast Interpretation Tool—a Monte Carlo technique for blending climatic distributions with probabilistic forecasts

Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.

International Journal of Climatology↗
USGS2005

Recent drought tendencies in Ethiopia and equatorial-subtropical eastern Africa

This report presents an analysis of Ethiopian rainfall from 1960 through 2004, based on 186 gauges in Ethiopia and 373 gauges from surrounding countriesi. Substantial post1997 declines in March-September rainfall have been observed in the northeast, southeast and southwestern portions of the country, a result corroborated by several independent data sourcesii. The observed rainfall reductions have been accompanied by an increase in millions needing food aid (Figure 1). The greatest percent decrease has occurred during the Belg (March-May) season, though Kiremt (June-September) rains have also diminished in many areas. This recent dryness may be linked to a warming tendency in the southern Indian Oceaniii, and is likely to impact densely populated and/or water-insecure regions of southern and eastern Ethiopia. These areas face declines in rainfall and increasing demands on land, water and agriculture. Recent dryness, dropping coffee prices, rising population and land degradation combine to create mounting destitution that must be countered with innovative planning and policiesiv. This report is organized as follows. Section 1 provides context for our results by summarizing recent trends in food aid, precipitation and the regional climate. Section 2 quantifies the water availability situation by analyzing potentially available runoff. Section 3 describes the results of our historical rainfall analysis. Section 4 relates the observed rainfall variations to circulation and sea surface temperature patterns in the Indian and Pacific Oceans. Section 5 briefly summarizes our results.

Equatorial-subtropical Africa↗