scholarly journals Discontinuous Daily Temperatures in the WATCH Forcing Datasets

2015 ◽  
Vol 16 (1) ◽  
pp. 465-472 ◽  
Author(s):  
Henning W. Rust ◽  
Tim Kruschke ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
Uwe Ulbrich
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Monthly Means ◽  
Tropical Africa ◽  
Average Temperature ◽  
Surface Models ◽  
The Tropics

Abstract The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

scholarly journals Assessment of MERRA-2 Land Surface Energy Flux Estimates

2018 ◽  
Vol 31 (2) ◽  
pp. 671-691 ◽  
Author(s):  
Clara S. Draper ◽  
Rolf H. Reichle ◽  
Randal D. Koster
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Longwave Radiation ◽  
Research Unit ◽  
Surface Energy Budget ◽  
Shortwave Radiation ◽  
Boreal Summer ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Global Land

In the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here aspects of the MERRA-2 land surface energy budget and 2-m air temperatures [Formula: see text] are assessed. For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W m−2), the sensible heating (by 6 W m−2), and the downwelling shortwave radiation (by 14 W m−2) while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10–15 W m−2 each). These results differ only slightly from those for NASA’s previous reanalysis, MERRA. Comparison to various gridded reference datasets over boreal summer (June–August) suggests that MERRA-2 has particularly large positive biases (>20 W m−2) where LH is energy limited and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during boreal summer, the globally averaged anomaly correlations [Formula: see text] with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs Global Land Evaporation Amsterdam Model data) and the daily maximum T2m (from 0.69 to 0.75 vs Climatic Research Unit data). In regions where [Formula: see text] is particularly sensitive to the precipitation corrections (including the central United States, the Sahel, and parts of South Asia), the changes in the [Formula: see text] [Formula: see text] are relatively large, suggesting that the observed precipitation influenced the [Formula: see text] performance.

scholarly journals Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

2020 ◽  
Vol 13 (9) ◽  
pp. 3975-3993 ◽  
Author(s):  
Miguel Nogueira ◽  
Clément Albergel ◽  
Souhail Boussetta ◽  
Frederico Johannsen ◽  
Isabel F. Trigo ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Added Value ◽  
Vegetation Coverage ◽  
Daily Maximum ◽  
Cold Bias ◽  
Weather Forecasts ◽  
Surface Models

Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover.  In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

scholarly journals WATCH: Current Knowledge of the Terrestrial Global Water Cycle

2011 ◽  
Vol 12 (6) ◽  
pp. 1149-1156 ◽  
Author(s):  
Richard Harding ◽  
Martin Best ◽  
Eleanor Blyth ◽  
Stefan Hagemann ◽  
Pavel Kabat ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Current Knowledge ◽  
Water Model ◽  
Grand Challenge ◽  
Global Water Cycle ◽  
Wide Range ◽  
Surface Models ◽  
Intercomparison Project ◽  
Global Water

Abstract Water-related impacts are among the most important consequences of increasing greenhouse gas concentrations. Changes in the global water cycle will also impact the carbon and nutrient cycles and vegetation patterns. There is already some evidence of increasing severity of floods and droughts and increasing water scarcity linked to increasing greenhouse gases. So far, however, the most important impacts on water resources are the direct interventions by humans, such as dams, water extractions, and river channel modifications. The Water and Global Change (WATCH) project is a major international initiative to bring together climate and water scientists to better understand the current and future water cycle. This paper summarizes the underlying motivation for the WATCH project and the major results from a series of papers published or soon to be published in the Journal of Hydrometeorology WATCH special collection. At its core is the Water Model Intercomparison Project (WaterMIP), which brings together a wide range of global hydrological and land surface models run with consistent driving data. It is clear that we still have considerable uncertainties in the future climate drivers and in how the river systems will respond to these changes. There is a grand challenge to the hydrological and climate communities to both reduce these uncertainties and communicate them to a wider society.

scholarly journals The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

2014 ◽  
Vol 6 (1) ◽  
pp. 61-68 ◽  
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Temperature Data ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Data Set ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

On the added value of improving the spatial representation and seasonal variations of vegetation cover in land surface models for simulated land surface temperature

2020 ◽  
Author(s):  
Miguel Nogueira ◽  
Clément Albergel ◽  
Souhail Boussetta ◽  
Frederico Johanssen ◽  
Emanuel Dutra
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Added Value ◽  
Vegetation Coverage ◽  
Daily Maximum ◽  
Cold Bias ◽  
Earth Observations ◽  
Surface Models

<p>Earth observations were used to evaluate and improve the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models - the European Center for Medium Range Weather Forecasting (ECMWF) Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) and the Méteo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004-2015 period.</p><p>The results show that the daily maximum LST simulated by HTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility Land Surface Analysis (LSA-SAF), reaching magnitude larger than 10ºC over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a misrepresentation of the vegetation cover.  In contrast, SURFEX simulations did not had such a cold bias. This was due to the better representation of vegetation coverage in SURFEX, which uses an updated land cover dataset (ECOCLIMAP II) and an interactive vegetation evolution, representing seasonality.</p><p>The representation of vegetation over Iberia in HTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) Leaf Area Index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement vegetation includes a clumping approach to introduce seasonality to the vegetation coverage. The results show significant added value, removing the daily maximum LST summer cold bias completely while never reducing the accuracy over all seasons and hours of the day.</p><p>This work has important implications: First, LST is a key variable in surface-atmosphere energy and water exchanges and, thus, its accurate representation in earth system models is very important. Second, HTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis, hence systematic errors are propagated into these products. Indeed, we show that the summer daily maximum LST cold bias over Iberia in HTESSEL is present in the widely used ECMWF fifth generation reanalysis (ERA5) and fourth generation reanalysis (ERA-Interim).  Finally, our results provide hints into the interaction between vegetation land-atmosphere exchanges, highlight the consistent relevance of the vegetation cover and seasonality in representing land surface temperature in both models, and how earth observations play a critical role for constraining and improving weather and climate simulations.</p>

scholarly journals SimSphere model sensitivity analysis towards establishing its use for deriving key parameters characterising land surface interactions

2014 ◽  
Vol 7 (5) ◽  
pp. 1873-1887 ◽  
Author(s):  
G. P. Petropoulos ◽  
H. M. Griffiths ◽  
T. N. Carlson ◽  
P. Ioannou-Katidis ◽  
T. Holt
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Water Cycle ◽  
Surface Interactions ◽  
Distribution Functions ◽  
Probability Distribution Functions ◽  
Surface Models ◽  
Significant Step ◽  
Key Variables ◽  
Edge Sensitivity

Abstract. Being able to accurately estimate parameters characterising land surface interactions is currently a key scientific priority due to their central role in the Earth's global energy and water cycle. To this end, some approaches have been based on utilising the synergies between land surface models and Earth observation (EO) data to retrieve relevant parameters. One such model is SimSphere, the use of which is currently expanding, either as a stand-alone application or synergistically with EO data. The present study aimed at exploring the effect of changing the atmospheric sounding profile on the sensitivity of key variables predicted by this model assuming different probability distribution functions (PDFs) for its inputs/outputs. To satisfy this objective and to ensure consistency and comparability to analogous studies conducted previously on the model, a sophisticated, cutting-edge sensitivity analysis (SA) method adopting Bayesian theory was implemented on SimSphere. Our results did not show dramatic changes in the nature or ranking of influential model inputs in comparison to previous studies. Model outputs examined using SA were sensitive to a small number of the inputs; a significant amount of first-order interactions between the inputs was also found, suggesting strong model coherence. Results showed that the assumption of different PDFs for the model inputs/outputs did not have an important bearing on mapping the most responsive model inputs and interactions, but only the absolute SA measures. This study extends our understanding of SimSphere's structure and further establishes its coherence and correspondence to that of a natural system's behaviour. Consequently, the present work represents a significant step forward in the global efforts on SimSphere verification, especially those focusing on the development of global operational products from the model synergy with EO data.

scholarly journals The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

2011 ◽  
Vol 15 (5) ◽  
pp. 1675-1698 ◽  
Author(s):  
W. A. Dorigo ◽  
W. Wagner ◽  
R. Hohensinn ◽  
S. Hahn ◽  
C. Paulik ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Water Cycle ◽  
Soil Moisture Data ◽  
Time Period ◽  
Surface Models ◽  
Different Depths ◽  
Operational Networks

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

scholarly journals The CRUTEM4 land-surface air temperature dataset: construction, previous versions and dissemination via Google Earth

2013 ◽  
Vol 6 (2) ◽  
pp. 597-619
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Markup Language ◽  
Online Access ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature version 4) land-surface air temperature dataset is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this dataset: online access to monthly, seasonal and annual data values and timeseries graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 dataset. A mathematical description of the construction of the CRUTEM4 dataset (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the dataset used in a particular study. The CRUTEM4 dataset used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

scholarly journals Recent Changes in Surface Humidity: Development of the HadCRUH Dataset

2008 ◽  
Vol 21 (20) ◽  
pp. 5364-5383 ◽  
Author(s):  
Katharine M. Willett ◽  
Philip D. Jones ◽  
Nathan P. Gillett ◽  
Peter W. Thorne
Keyword(s):  
Relative Humidity ◽  
Northern Hemisphere ◽  
Research Unit ◽  
Specific Humidity ◽  
Climatic Research Unit ◽  
Global Trends ◽  
Dewpoint Temperature ◽  
Key Driver ◽  
Hadley Centre ◽  
The Tropics

Abstract Water vapor constitutes the most significant greenhouse gas, is a key driver of many atmospheric processes, and hence, is fundamental to understanding the climate system. It is a major factor in human “heat stress,” whereby increasing humidity reduces the ability to stay cool. Until now no truly global homogenized surface humidity dataset has existed with which to assess recent changes. The Met Office Hadley Centre and Climatic Research Unit Global Surface Humidity dataset (HadCRUH), described herein, provides a homogenized quality controlled near-global 5° by 5° gridded monthly mean anomaly dataset in surface specific and relative humidity from 1973 to 2003. It consists of land and marine data, and is geographically quasi-complete over the region 60°N–40°S. Between 1973 and 2003 surface specific humidity has increased significantly over the globe, tropics, and Northern Hemisphere. Global trends are 0.11 and 0.07 g kg−1 (10 yr)−1 for land and marine components, respectively. Trends are consistently larger in the tropics and in the Northern Hemisphere during summer, as expected: warmer regions exhibit larger increases in specific humidity for a given temperature change under conditions of constant relative humidity, based on the Clausius–Clapeyron equation. Relative humidity trends are not significant when averaged over the landmass of the globe, tropics, and Northern Hemisphere, although some seasonal changes are significant. A strong positive bias is apparent in marine humidity data prior to 1982, likely owing to a known change in reporting practice for dewpoint temperature at this time. Consequently, trends in both specific and relative humidity are likely underestimated over the oceans.

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