Similarity assessment and adjustment of integrated temperature databases. Cuyo region, Argentina

Given the frequent spatial-temporal limitations and deficiencies of instrumental meteorological records, the use of alternative information sources, such as integrated databases, are important for analyses and studies of diverse nature. The research aim was to evaluate the accuracy of integrated databases of monthly temperature, belonging to Climate Research Unit, University of Delaware and Global Historical Climatology Network, gridded with a pixel size of 3,098.01 km2 (0.5º x 0.5º), surface area of 151,802.5 km2 and temporary length of 22 years (1993-2014), through the modified structural similarity index (mSSIM). The study area is located in central-western Argentina (between 30º and 35º S, and 71º and 66º W). The University of Delaware grid showed the best fit of the data series from 10 weather stations located in the study area. Therefore, a proposal was presented to increase similarity indices, especially for those cells without instrumental reference information. The study determined that by applying this modification, the gridded datasets increases the similarity of the measured data, especially in mountainous areas, where originally there were differences of more than 7.5 ºC between the gridded data and observed one. The proposal decreases these differences to average values below 1 ºC. The use and subsequent adjustment of these integrated databases, allows access to information in areas without meteorological records.

Quality Control and Processing of Cooperative Observer Program Hourly Precipitation Data

The National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA’s National Weather Service in 2013. Subsequently, NOAA’s National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset.

Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016

We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

Climate Change and Crime Revisited: An Exploration of Monthly Temperature Anomalies and UCR Crime Data

A growing body of research suggests a positive connection between climate change and crime, but few studies have explored the seasonal nature of that link. Here, we examine how the impact of climate change on crime may partly depend on specific times of the year as recent climatological research suggests that climate change may have a diverging impact during different times of the year. To do so, we utilize the largest, most current dataset of all main categories of reported crime by month and year in the United States—the Federal Bureau of Investigation’s (FBI) Uniform Crime Reports. We employ historical weather data collected by the Global Historical Climatology Network to measure climate change, and develop a procedure that weighs and connects these data to individual crime reporting agencies. We discover not only a positive association between climate change and crime but also substantial monthly variation in this association.

Rainfall Estimates on a Gridded Network (REGEN) – A global land-based gridded dataset of daily precipitation from 1950–2013

We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a GriddEd Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station timeseries were quality controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area average estimates of daily precipitation for global land areas on a 1° × 1° latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, Kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes, and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

The Global Historical Climatology Network Monthly Temperature Dataset, Version 4

We 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 with the GHCN-daily dataset and makes use of data from previous versions of GHCNm as well as data collated under the auspices of the International Surface Temperature Initiative. GHCNm v4 has many thousands of additional stations compared to version 3 (v3) both historically and with short time-delay updates. The greater number of stations as well as the use of records with incomplete data during the base period provides for greater global coverage throughout the record compared to earlier versions. Like v3, the monthly averages are screened for random errors and homogenized to address systematic errors. New to v4, uncertainties are calculated for each station series, and regional uncertainties scale directly from the station uncertainties. Correlated errors in the station series are quantified by running the homogenization algorithm as an ensemble. Additional uncertainties associated with incomplete homogenization and use of anomalies are then incorporated into the station ensemble. Further uncertainties are quantified at the regional level, the most important of which is for incomplete spatial coverage. Overall, homogenization has a smaller impact on the v4 global trend compared to v3, though adjustments lead to much greater consistency than between the unadjusted versions. The adjusted v3 global mean therefore falls within the range of uncertainty for v4 adjusted data. Likewise, annual anomaly uncertainties for the other major independent land surface air temperature datasets overlap with GHCNm v4 uncertainties.

SÉRIES CLIMÁTICAS EM GRADE DE PRECIPITAÇÃO E TEMPERATURA DO AR EM REGIÃO DE RELEVO COMPLEXO

Avaliaram-se séries climáticas (1960-91) de precipitação e temperatura do ar mensal de produtos em grade em relação às séries desses elementos observadas em estações meteorológicas do estado do Rio de Janeiro. As séries climáticas observadas foram obtidas nas estações do Instituto Nacional de Meteorologia, localizadas no estado do Rio de Janeiro. As séries dos produtos em grade foram extraídas nos pontos de grade (resolução 0,5 o x 0,5o) dos produtos do Global Precipitation Climatology Center (GPCC), The Global Historical Climatology Network (GHCN) ou Universidade de Delaware (UDEL) mais próximos das estações em estudo. Avaliou-se a precisão (coeficiente de determinação – r²) e exatidão (índice de concordância de Willmott – d e Raiz do Quadrado Médio do Erro - RQME) de cada produto em grade em relação às séries observadas. Os produtos em grade de precipitação (GPCC e UDEL) não tiveram precisão satisfatória (r² < 0,54 - GPCC e r² < 0,61 - UDEL), contudo sua exatidão (d > 0,59 - GPCC e d > 0,58 - UDEL) foi superior à precisão. Os erros observados para precipitação foram entre 59,5 e 125,8 mm. As séries em grade de temperatura tiveram maior precisão (r² > 0,41 - GHCN e r² > 0,35 - UDEL) e exatidão similar (d > 0,58 - GHCN e d > 0,65 - UDEL), com RQME entre 1,11 e 3,98 oC. Foram identificadas associações que elevam o erro dos produtos em grade para a região, tais como, o elevado gradiente altitudinal da área de estudo e o efeito continentalidade/maritimidade. Dentre os produtos em grade de precipitação, o GPCC apresentou melhor desempenho (maior precisão e exatidão) em relação à UDEL na maior parte das estações, enquanto para temperatura do ar, as séries em grade da UDEL se sobressaíram em comparação ao GHCN. É necessário desenvolver produtos climáticos de precipitação e temperatura do ar em grade precisos e exatos com alta resolução para o estado do Rio de Janeiro.

An Assessment of High-Resolution Gridded Temperature Datasets over California

High-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends (&gt;1°C decade−1) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.

Detection and Analysis of an Amplified Warming of the Sahara Desert

Evaluation of three reanalyses (ERA-Interim, NCEP-2, and MERRA) and two observational datasets [CRU and Global Historical Climatology Network (GHCN)] for 1979–2012 demonstrates that the surface temperature of the Sahara Desert has increased at a rate that is 2–4 times greater than that of the tropical-mean temperature over the 34-yr time period. While the response to enhanced greenhouse gas forcing over most of the globe involves the full depth of the atmosphere, with increases in longwave back radiation increasing latent heat fluxes, the dryness of the Sahara surface precludes this response. Changes in the surface heat balance over the Sahara during the analysis period are primarily in the upward and downward longwave fluxes. As a result, the warming is concentrated near the surface, and a desert amplification of the warming occurs. The desert amplification is analogous to the polar amplification of the global warming signal, which is concentrated at the surface, in part, because of the vertical stability of the Arctic atmosphere. Accompanying the amplified surface warming of the Sahara is a strengthening of both the summertime heat low and the African easterly jet and a weakening of the wintertime anticyclone and the low-level Harmattan winds. Potential implications of the desert amplification include decreases in mineral dust aerosols globally, decreases in wintertime cold air surge activity, and increases in Sahel rainfall.