Runoff and Streamflow

TerraClimate: Global, high-resolution gridded temperature, precipitation, and other water balance variables

ryanj — Sat, 14 Dec 2019 17:54:00 +0000

TerraClimate: Global, high-resolution gridded temperature, precipitation, and other water balance variables ryanj Sat, 12/14/2019 - 10:54

TerraClimate is a global gridded dataset of meteorological and water balance variables for 1958-present, available on a monthly timestep. Its relatively fine spatial resolution, global extent, and long length are a unique combination that fills a void in climate data. TerraClimate combines spatial climatology from WorldClim with time-varying information from the coarser resolution CRU TS4.0.

In addition to maximum and minimum temperatures and precipitation, TerraClimate provides derived variables including reference evapotranspiration, vapor pressure deficit, and PDSI. Water balance metrics including runoff, snow water equivalent, soil moisture, and climatic water deficit are calculated using a Thornthwaite-Mather climatic water-balance model (e.g., Willmott et al., 1985; Dobrowski et al., 2013) and extractable soil water storage capacity data (Wang-Erlandsson et al. 2016).

Key Strengths

Combines fine spatial resolution climatology with temporal information for 1958-present

In addition to standard monthly climate summaries, TerraClimate provides variables that are of more immediate use to surface hydroclimate for ecology and hydrology including runoff, actual evapotranspiration, soil moisture, and climatic water deficit

Data readily available for download or online visualization

Key Limitations

climatically aided interpolation interpolates anomalies from the coarser resolution parent product (e.g., CRU Ts at 0.5 degree resolution) to the higher-spatial resolution climatology. As such, sharp gradients in climate anomalies in montane or near coastal environments, will not be realized as TerraClimate will not capture temporal variability in climate measures at finer scales than its parent product

TerraClimate should not be viewed to provide an independent estimate of trends in variables such as temperature

Underlying uncertainties in the core datasets used, including negative precipitation biases in mountains of the western US inherited from WorldClim, or inhomogeneities in CRU TS or reanalysis, are entrained into TerraClimate . Also, uses a very simple water balance model.

Expert Developer Guidance

The following was contributed by Dr. John Abatzoglou, July 2019:

The TerraClimate dataset was developed to fill a void in the availability of freely available moderate-to-high spatial resolution global climate data that spans multiple decades. While datasets with similar attributes are available for individual countries or even continents, the paucity in comparable data across global terrestrial surfaces can be a barrier for end-users who need estimates of place-based climate data such as those often required in ecology and hydrology. TerraClimate provides a six-decade record of monthly climate data for global terrestrial surfaces on a ~4-km (1/24th degree) grid by blending desirable spatial attributes from WorldClimV2 with desirable temporal attributes from CRU Ts4.0 and reanalyses products through climatically aided interpolation.

In addition to providing output of first-order climate measures such as temperature and precipitation, TerraClimate provides derived climate variables that are often of value for climate impact assessments including reference evapotranspiration (Penman-Monteith ASCE), vapor pressure deficit, and PDSI. A modified Thornthwaite-Mather climatic water-balance model (e.g., Willmott et al., 1985; Dobrowski et al., 2013).and extractable soil water storage capacity data (Wang-Erlandsson et al. 2016) are used to compute water balance metrics runoff, snow water equivalent, soil moisture, actual evapotranspiration, and climatic water deficit.

These data can be used in species distribution modeling, to approximate local variability and changes where station-based data are lacking or derived variables are preferred, and for climate-impact analyses in ecological, agricultural, and hydrological systems cases where spatial attributes of climate may be preferred over coarser resolution data. Data are stored in Network Common Data Form (NetCDF) format. Both the files and THREDDS based access to data are available at the University of Idaho’s Northwest Knowledge Network http://www.climatologylab.org/terraclimate.html. A point-based data extraction tool is also available for users who only need to access time series of data for a few locations: https://climatetoolbox.org/tool/Point-Data-Download. Data are also available through Google Earth Engine. Data are updated annually when parent datasets become available. Metadata files are also produced which provide information on the number of stations (from CRU Ts4.0) that contribute to the temporal variability of TerraClimate for temperature, precipitation, and vapor pressure. Estimates from reanalyses (JRA-55) are used for anomalies in cases where zero stations contribute. Temporal variability on solar radiation and wind speed comes entirely from reanalyses.

Key Strengths

Moderate-high spatial resolution monthly climate layers from 1958-present;
In addition to standard monthly climate summaries, TerraClimate provides variables that are of more immediate use to surface hydroclimate for ecology and hydrology including runoff, actual evapotranspiration, soil moisture, and climatic water deficit;

Key Limitations

First, climatically aided interpolation interpolate anomalies from coarser resolution parent product (e.g., CRU Ts at 0.5 degree resolution) to the higher-spatial resolution climatology. As such, sharp gradients in climate anomalies in montane or near coastal environments, will not be realized as TerraClimate will not capture temporal variability in climate measures at finer scales than its parent product. Likewise, due to the reliance on other datasets, TerraClimate should not be viewed to provide an independent estimate of trends in variables such as temperature. Underlying uncertainties in the core datasets used, including negative precipitation biases in mountains of the western US inherited from WorldClim, or inhomogeneities in CRU TS or reanalysis, are entrained into TerraClimate (e.g., Abatzoglou et al., 2018).

Therefore, while TerraClimate does not explicitly use station observations, it relies on products that make heavy use of station records, and model assimilation in reanalysis including that which spans the satellite era. Non-climatic features present in parent datasets may be incorporated into TerraClimate. Efforts have been made to correct for spurious trends in solar radiation and wind speeds in the JRA by incorporating two different reanalysis products to span the 1958-2017 period: JRA-55C 1958-1978; ERA-Interim 1979-2017. We bias corrected monthly mean data using an overlapping 10-year period (1979-1988) to facilitate a single time series of anomalies in downward shortwave flux and the surface and 10-m wind speed. Future updates will likely incorporate the single long-term data from ERA-5.

TerraClimate uses a very simple water-balance model to estimate surface water fluxes. This approach does not account for variability in vegetation type or abundance, or intricacies of water storage and fluxes found in more involved hydrologic models. Instead, TerraClimate uses a static reference vegetation layer, a one-dimensional soil water holding capacity derived from remote sensing, and a basic temperature-based snow model. ##

COREv2 Air-Sea Surface Fluxes

ryanj — Fri, 20 Jan 2012 15:20:00 +0000

COREv2 Air-Sea Surface Fluxes ryanj Fri, 01/20/2012 - 08:20

The Coordinated Ocean Research Experiments version 2 (COREv2) data set consists of a globally complete set of air-sea fluxes of momentum, heat and freshwater for 1948-2009 at monthly resolution. This data set serves as a set of common atmospheric boundary conditions that has been used by the ocean modeling community to force and compare a diverse set of coupled ocean-sea ice models. The primary data source is the NCEP/NCAR R1 reanalysis product. To reduce climatological biases in COREv2, the main NCEP/NCAR R1 fields used to compute the turbulent air-sea fluxes have been bias corrected on the basis of comparison with more reliable satellite and in-situ data. Satellite-based radiation data and blended satellite-gauge precipitation data are used in place of the reanalysis data for 1984-2009 and 1979-2009, respectively. Prior to the satellite era, COREv2 does not contain realistic time-varying radiation and precipitation fluxes. Another disadvantage of COREv2 is that there are no estimates of uncertainties.

Key Strengths

Contains a globally complete set of air-sea momentum, heat and freshwater fluxes for 1948-2009 at monthly resolution

COREv2 boundary conditions yield a small, globally averaged heat imbalance of 1.6 W/m^2 when coupled to observed SSTs, allowing for long model integrations with minimal ocean drift

Source data from the NCEP/NCAR R1 (near surface wind, potential temperature, specific humidity, and density) were bias adjusted to reduce climatological biases

Key Limitations

Lack of realistic variability in the precipitation and radiation fluxes prior to the satellite era

No uncertainty estimates

Expert User Guidance

Expert Developer Guidance

The following was contributed by Dr. Stephen Yeager, November, 2015:

This data set constitutes a complete set of global air-sea heat and freshwater flux components from 1948 through 2009 at monthly resolution. The atmospheric data set used is a combination of state fields from atmospheric reanalysis and satellite-derived flux measurements that is referred to as "COREv2"--Coordinated Ocean-ice Reference Experiments data set, version 2 (Large and Yeager 2009, hereafter LY09). COREs are coordinated model intercomparison efforts undertaken by the international ocean and sea ice modelling communities in which a diverse set of coupled ocean--sea-ice models are forced with common atmospheric boundary conditions (Griffies et al. 2009). The COREv2 atmospheric data set documented in LY09 is based primarily on surface fields from the NCEP/NCAR R1 reanalysis product (referred to hereafter as NCEP) that are available from 1948 to present, but it incorporates satellite and in situ measurements to either adjust or replace certain NCEP fields deemed unreliable. As documented in Large and Yeager (2004) and later in LY09, a key motivation for generating the COREv2 atmospheric data set is to provide a full suite of best-available, historical boundary conditions for ocean and sea-ice modelling studies. The COREv2 data set has been promoted and disseminated by the CLIVAR Ocean Model Development Panel (http://www.clivar.org/clivar-panels/omdp/core) for coordinated studies (e.g., Danabasoglu et al. 2014) that are collected in a virtual special issue of the journal Ocean Modelling (http://www.journals.elsevier.com/ocean-modelling/news/core-ii-virtual-special-issue/). The COREv2 data and release notes can be downloaded from a server at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL; http://data1.gfdl.noaa.gov/nomads/forms/core/COREv2.html).

In COREv2, NCEP surface radiation is replaced with daily surface downwelling longwave and shortwave radiative fluxes from the GISS ISCCP-FD data set (Zhang et al. 2004) that spans 1984-2009. NCEP precipitation is discarded in favor of a blended product (spanning 1979-2009) that combines GPCP (Global Precipitation Climatology Project; http://precip.gsfc.nasa.gov/), CMAP (Climate Prediction Center Merged Analysis of Precipitation; http://www.esrl.noaa.gov/psd/data/gridded/data.cmap.html), and monthly climatology over the Arctic (see LY09 for details). Other atmospheric state fields required for computing turbulent air-sea fluxes (i.e., near surface wind, potential temperature, specific humidity, and density) come from NCEP, but with adjustments applied to reduce climatological biases. The biases (and corresponding adjustments) are determined by comparing with more reliable satellite or in situ measurements that themselves are too regional or short in duration to serve as forcing data for historical ocean--sea-ice simulations. As implied above, not all of the data streams in COREv2 are available over the full 1948-2009 time period; climatological fill is used to fill in missing years (see LY09 for details). The COREv2 release notes also describe the historical continental freshwater discharge data set (based on Dai et al. 2009) that is incorporated as an ocean forcing field. Although not strictly an air-sea flux, this runoff flux is included in the COREv2 flux data set for completeness.

While COREv2 was developed for the purpose of forcing prognostic ocean and sea ice models, it can alternatively be "coupled" to observed, historical ocean and sea ice fields through bulk formulae to yield an estimation of historical air-sea fluxes. That is the procedure used to generate this COREv2 flux data set.

Turbulent air-sea fluxes (evaporation, sensible and latent heat fluxes, and wind stress) are obtained by coupling the COREv2 atmospheric data stream with monthly observed sea surface temperature (Hurrell et al. 2008) using the standard bulk formulae of the Community Earth System Model (CESM; see LY09). These turbulent fluxes are computed at 6-hourly temporal resolution (using linear interpolation of the monthly observed SST field), and then time-averaged to yield monthly-mean fluxes. Daily fractional sea-ice concentration from satellite measurements from the National Snow and Ice Data Center (NSIDC) are used to mask the air-sea fluxes in polar regions. Together with the radiation and precipitation fluxes, these constitute a complete set of historical air-sea fluxes.

From the perspective of ocean modelling, a key advantage of the COREv2 boundary conditions is that, when coupled to observed SST as outlined above, they yield a globally-averaged, climatological (1984-2006) air-sea heat flux of only 1.6 W/m^2. This relatively small heat imbalance, which LY09 argue is largely consistent with available estimates of long-term ocean warming, allows for long integrations over multiple forcing cycles with minimal ocean drift (assuming the model is capable of accurate simulation of observed SST and sea ice). The widespread use of COREv2 in the ocean modelling community makes this a natural companion flux data set to use for evaluating model fluxes. The fidelity of COREv2 fluxes cannot easily be ascertained, but LY09 show that they imply oceanic heat and freshwater transports that are compatible with direct observations and they compare favorably with other air-sea flux products (see Valdivieso et al. 2015). On obvious disadvantage of COREv2 fluxes is the lack of realistically varying precipitation and radiation fluxes prior to the satellite era. COREv2 will probably exhibit less agreement with in situ buoy measurements than products like OAFlux (Yu and Weller, 2007) that use these data in a more systematic way to constrain fluxes. Finally, another drawback of the COREv2 flux data set is the lack of error estimates, because these are likely to be substantial. Flux comparison papers like Valdivieso et al. (2015) give a sense of the uncertainties inherent to air-sea flux estimation.

Freshwater Discharge: 1948-2004

ryanj — Tue, 15 Nov 2011 19:35:00 +0000

Freshwater Discharge: 1948-2004 ryanj Tue, 11/15/2011 - 12:35

"A dataset of historical monthly streamflow at the farthest downstream stations for the world’s 925 largest ocean-reaching rivers has been created for community use. Compared with previous studies, the new dataset has improved homogeneity and enables more reliable assessments of decadal and long-term changes in continental freshwater discharge into the oceans." The data are in the form of time series.