Data Issues for Latin America

Dr. Hugo G. Hidalgo

School of Physics

University of Costa Rica

GEOSS Support for the IPCC Assessments

February 2011

hugo.hidalgo@ucr.ac.cr

Hugo G. Hidalgo Universidad de Costa Rica 2

Acknowledgements

Thank you to GEOSS for financial support for this visit. The author is partially supported by projects VI-805-A9-22, VI-805-A9-532 and VI-808-A9-180 of the University of Costa Rica.Thank you to the School of Physics for the support to this visit.Thank you to FUNDEVI/UCR for partial additional support

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Data issues for Latin America

Sources (surface observations, upper air, remote sensing) Temporal and spatial coverage (missing data) Quality control (outliers, temporal and spatial inconsistencies) Costs of data (data are in many cases not free) Availability (restricted use, release agreements) Format (including digitalization and file format, temporal and

spatial resolution) Reliability (changes in station location or conditions, changes

in equipment or observer, history of calibration and maintenance of instruments)

Adequacy for specific uses (related to all of the above)

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Other issues for Latin America

Complex topography Large variety of climates and microclimates Rich biodiversity Developing economies Vulnerable societies

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Specific uses

Restrospective (hydro) climatic variability analysis Extreme events analysis (considering different time scales

of floods and droughts) Detection and Attribution of climate change Climate change impacts assessments on human and

environmental systems (agriculture, hydropower generation, ecology, social-economic, wildfire potential, public health, water quality and others)

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Hydrologic simulations are needed

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Streamflow data for calibration 1969 to 1979 (GRDC)

Monthly streamflowgages

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LA impacts AR4

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds. Cambridge University Press, Cambridge, UK, 976 pp

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Historical trends in rainfall

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds. Cambridge University Press, Cambridge, UK, 976 pp

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Spatial and temporal coverage

CIGEFI's Networkof daily precipitationstations

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Spatial and temporal coverage

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Precip significant correlation distance (degrees)

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The problem of missing necessary data

Following the International Hydrological Decade (IHD, 1965-1974) the number of stations in the region increased dramatically.

Since the 1990s many monitoring programs have been dismantled.

Satellite data cannot replace surface observations (maybe in the future)

The current network does not cover large parts of the region (which have large spatial variations due to the richness of microclimates)

It has not been proven that the 140 stations available in Central America during the 1980s are enough to represent climate variability adequately.

Saving money by closing stations is foolish, the costs will be reflected in other items such as over-dimensioned dams and bridges or the collapse of dams and bridges.

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AVAILABILITY OF SURFACE OBSERVATIONS

http://www.atmosfera.cl/HTML/antiguo/TEMAS/INSTRUMENTACION/FIG1b.htm

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National Climatic Data Center: Global Historical Climatology

Network (GHCN)

Core elements: Daily precipitation, snowfall, snowdepth, tmax and tmin data. Quality controlled.

Temp Precip

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Integrated Surface Database: Summary of the Day

Many parameters: including daily precipitation,tmax and tmin data. Quality controlled.

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Examples of Ground-based Gridded Datasets

Global Meteorological Forcing Dataset: The base meteorological data consist of daily time-series for the period of 1950 through 1999 of precipitation, maximum temperature, minimum temperature, and wind speed. Data from a variety of sources were compiled and gridded to a resolution of 1/2-degree over all global land areas. Maurer, E.P., J.C. Adam, and A.W. Wood, 2009, Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America, Hydrology and Earth System Sciences 13, 183-194.

Global Meteorological Forcing Dataset for land surface modeling: A global 50-yr (1948-2000) dataset of meteorological forcings derived by combining reanalysis with observations. Available at 1.0-degree spatial resolution and 3-hourly, daily and monthly temporal resolution. Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling, J. Climate, 19 (13), 3088-3111

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Examples of Satellite + Ground-based Gridded Datasets

CRN073: Daily and monthly precip dataset covering mesoamerica, 0.5 x 0.5 degrees, 1958-2000. Magana et al. 2003

CMAP: Global precip, Monthly 2.5 x 2.5 degrees, 1979-2009. Xie, P. and P. A. Arkin, 1996: Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions. J. Climate, 9, 840 -858. Xie, P. and P. A. Arkin, 1997: Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates and Numerical Model Outputs. BAMS, 78, 2539-2558.

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Webber and Willmott:precip and temp

From GHCN Gridded 0.5 x 0.5 degrees Monthly 1960-1990 No tmax, tmin Extremes:

Drought extremes: ok,

short term events like floods: not ok

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Latin America data needs

We need to estimate climate variability and work on covering all the subregions using monitoring stations.

We need to develop more the use of satellite data to become less dependent of the surface data, especially in hard to reach areas.

Politicians and decision-makers must provide funding to improve monitoring networks.

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Improving data issues

Create regional databases from global data with homogeneous quality control

Reformat data and create archives in standard format for the region Need to make inventories of data that are not free or not available in the

internet Need to establish collaborations with researchers from several countries

to use restricted data in regional studies More validation of satellite data is needed More integrated regional analysis of the impacts of climate change are

needed Make available free data products (e.g. regional interpolated datasets) Improving modeling and monitoring networks is key More land surface and hydrologic data are needed

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Thank you!

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Need for reducing uncertainties

“By the 2020s, the net increase in the number of people experiencing water stress due to climate change is likely to be between 7 and 77 million (medium confidence). While, for the second half of the century, the potential water availability reduction and the increasing demand from an increasing regional population would increase these figures to between 60 and 150 million. [13.4.3]”

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds. Cambridge University Press, Cambridge, UK, 976 pp