Claude N. Williams

Using the Special Sensor Microwave/Imager to Monitor Land Surface Temperatures, Wetness, and Snow Cover

Abstract The worldwide network of in situ land surface temperatures archived in near-real time at the National Climatic Data Center (NCDC) has limited applications, since many areas are poorly represented or provide no observations. Satellite measurements offer a possible way to fill in the data voids and obtain a complete map of surface temperature over the entire globe. A method has been developed to calculate near-surface temperature using measurements from the Special Sensor Microwave/Imager (SSM/I). To accomplish this, the authors identify numerous surface types and make dynamic adjustments for variations in emissivity. Training datasets were used to define the relationship between the seven SSM/I channels and the near-surface temperature. For instance, liquid water on the surface reduces emissivity; therefore, the authors developed an adjustment to correct for this reduction. Other surface types (e.g., snow, ice, and deserts) as well as precipitation are identified, and numerous adjustments and/or f...

Calibration and Verification of Land Surface Temperature Anomalies Derived from the SSM/I

Abstract The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first–order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show...

A Blended Satellite–In Situ Near–Global Surface Temperature Dataset

A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1° × 1° grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations.

Reassessing changes in diurnal temperature range: Intercomparison and evaluation of existing global data set estimates

Changes in diurnal temperature range (DTR) over global land areas are compared from a broad range of independent data sets. All data sets agree that global-mean DTR has decreased significantly since 1950, with most of that decrease occurring over 1960–1980. The since-1979 trends are not significant, with inter-data set disagreement even over the sign of global changes. Inter-data set spread becomes greater regionally and in particular at the grid box level. Despite this, there is general agreement that DTR decreased in North America, Europe, and Australia since 1951, with this decrease being partially reversed over Australia and Europe since the early 1980s. There is substantive disagreement between data sets prior to the middle of the twentieth century, particularly over Europe, which precludes making any meaningful conclusions about DTR changes prior to 1950, either globally or regionally. Several variants that undertake a broad range of approaches to postprocessing steps of gridding and interpolation were analyzed for two of the data sets. These choices have a substantial influence in data sparse regions or periods. The potential of further insights is therefore inextricably linked with the efficacy of data rescue and digitization for maximum and minimum temperature series prior to 1950 everywhere and in data sparse regions throughout the period of record. Over North America, station selection and homogeneity assessment is the primary determinant. Over Europe, where the basic station data are similar, the postprocessing choices are dominant. We assess that globally averaged DTR has decreased since the middle twentieth century but that this decrease has not been linear.

Reassessing changes in diurnal temperature range: A new data set and characterization of data biases

It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative’s databank effort and applies the National Centers for Environmental Information’s automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (-0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic “raw” data. Most of the estimated DTR reduction occurred over 1960–1980. In several regions DTR has apparently increased over 1979–2012, while globally it has exhibited very little change (-0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century).

Cross-Sectional Analyses of Climate Change Impacts

The authors explore the use of cross-sectional analysis to measure the impacts of climate change on agriculture. The impact literature, using experiments on crops in laboratory settings combined with simulation models, suggests that agriculture will be strongly affected by climate change. The extent of these effects varies by country and region. Therefore, local experiments are needed for policy purposes, which becomes expensive and difficult to implement for most developing countries. The cross-sectional technique, as an alternative approach, examines farm performance across a broad range of climates. By seeing how farm performance changes with climate, one can estimate long-run impacts. The advantage of this approach is that it fully captures adaptation as each farmer adapts to the climate they have lived in. The technique measures the full net cost of climate change, including the costs as well as the benefits of adaptation. However, the technique is not concern-free. The four chapters in this paper examine important potential concerns of the cross-sectional method and how they could be addressed, especially in developing countries. Data availability is a major concern in developing countries. The first chapter looks at whether estimating impacts using individual farm data can substitute using agricultural census data at the district level that is more difficult to obtain in developing countries. The study, conducted in Sri Lanka, finds that the individual farm data from surveys are ideal for cross-sectional analysis. Another anticipated problem with applying the cross-sectional approach to developing countries is the absence of weather stations, or discontinued weather data sets. Further, weather stations tend to be concentrated in urban settings. Measures of climate across the landscape, especially where farms are located, are difficult to acquire. The second chapter compares the use of satellite data with ground weather stations. Analyzing these two sources of information, the study reveals that satellite data can explain more of the observed variation in farm performance than ground station data. Because satellite data are readily available for the entire planet, the availability of climate data will not be a constraint. A continuing debate is whether farm performance depends on just climate normals-the average weather over a long period of time-or on climate variance (variations away from the climate normal). Chapter 3 reveals that climate normals and climate variance are highly correlated. By adding climate variance, the studies can begin to measure the importance of weather extremes as well as normals. A host of studies have revealed that climate affects agricultural performance. Since agriculture is a primary source of income in rural areas, it follows that climate might explain variations in rural income. This is tested in the analysis in Chapter 4 and shown to be the case. The analysis reveals that local people in rural areas could be heavily affected by climate change even in circumstances when the aggregate agricultural sector in the country does fine.

Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network

Numerous inhomogeneities including station moves, instrument changes, and time of observation changes in the U.S. Historical Climatological Network (USHCN) complicate the assessment of long-term temperature trends. Detection and correction of inhomogeneities in raw temperature records have been undertaken by NOAA and other groups using automated pairwise neighbor comparison approaches, but these have proven controversial due to the large trend impact of homogenization in the United States. The new U.S. Climate Reference Network (USCRN) provides a homogenous set of surface temperature observations that can serve as an effective empirical test of adjustments to raw USHCN stations. By comparing nearby pairs of USHCN and USCRN stations, we find that adjustments make both trends and monthly anomalies from USHCN stations much more similar to those of neighboring USCRN stations for the period from 2004 to 2015 when the networks overlap. These results improve our confidence in the reliability of homogenized surface temperature records.

The Global Historical Climatology Network Monthly Temperature Dataset, Version 4

AbstractWe describe a fourth version of the Global Historical Climatology Network (GHCN)-monthly (GHCNm) temperature dataset. Version 4 (v4) fulfills the goal of aligning GHCNm temperature values w...

Using the Special Sensor Microwave Imager to Monitor Surface Wetness and Temperature

The current network of in situ stations is inadequate for monitoring regional temperature and moisture anomalies across the land surface, leaving the climate monitoring community insufficient information to identify spatial structure and variations over many areas of the world. Therefore, we need to blend satellite observations with in situ data to obtain global coverage. In order to accomplish this task, we have calibrated and independently validated an algorithm that derives land surface temperatures from the Special Sensor Microwave Imager (SSMI). The goal of this exercise is to blend both the in situ and satellite data sets into one superior product, then merge this product with an sea surface temperature anomaly field form the same base period. The value of the global product has extremely valuable applications to climate modeling community, since it can serve as a validation tool and/or direct input to the surface parameterization, allowing the radiation feed back to be realistically grounded on surface temperature and humidity observations.