PRECIS

PRECIS
Name	PRECIS
Developer	WHO
Released	1990s
Programming language	C/C++
Operating system	Cross-platform
License	Proprietary

PRECIS

PRECIS is an acronym denoting a regional climate modelling system developed to downscale global climate information for impact assessment and adaptation planning. The system has been used by a range of United Nations agencies, national meteorological services such as the Met Office and NOAA, research institutes including the Hadley Centre and CSIRO, and regional bodies like the Caribbean Community and African Union for generating fine‑scale projections linked to IPCC assessments and UNFCCC processes.

Overview

PRECIS (Providing Regional Climates for Impacts Studies) is a regional climate model originally designed to translate coarse output from coupled atmosphere‑ocean models such as those used in Coupled Model Intercomparison Project phases into higher‑resolution fields suitable for impact modelling in sectors exemplified by FAO assessments, WHO health risk analyses, and UNEP ecosystem evaluations. The tool integrates parameterizations and physical schemes employed by global models like those at the Met Office Hadley Centre with nesting methods comparable to implementations at GFDL and Max Planck Institute for Meteorology to produce regional projections for vulnerability reduction and adaptation planning used by World Bank projects and bilateral development agencies.

History and Development

PRECIS evolved from regionalization efforts in the 1990s when climate downscaling became essential to translate IPCC scenarios into actionable information for national adaptation. Initial development drew on dynamical core components validated against datasets from Global Atmospheric Research Program initiatives and observational programs such as World Meteorological Organization networks. Partnerships between the Hadley Centre and UK aid bodies catalyzed distribution to national meteorological services across Asia, Africa, Latin America, and Small Island Developing States, facilitating capacity building through workshops involving institutions like Universidade de São Paulo, Indian Institute of Tropical Meteorology, Kenya Meteorological Department, and University of the West Indies.

Subsequent iterations integrated boundary condition handling compatible with atmosphere‑ocean coupled outputs from modelling centers such as NOAA GFDL, ECMWF, and the NCAR, enabling application across RCP and SRES scenario families referenced in successive IPCC assessment reports. Collaboration with regional research consortia including the South African Weather Service and Asian Development Bank further refined usability and documentation.

Design and Architecture

PRECIS uses a limited‑area dynamical framework that nests inside global model fields to resolve mesoscale features. Its architecture incorporates physical parameterizations for radiation, convection, surface processes and land‑sea interactions adapted from parent models at the Met Office Hadley Centre and informed by schemes tested at NASA and NCAR. The system accepts lateral boundary forcing from global coupled models such as those participating in CMIP, and supports multiple grid configurations to target domains covering entire regions like Southeast Asia, Amazon Basin, Sahel, and Caribbean Sea.

Key modules handle initialization, boundary condition interpolation, surface coupling, and output post‑processing compatible with impact models used by FAO, WHO, UNEP, and academic groups at MIT and University of Oxford. The codebase emphasizes portability and has been compiled on platforms ranging from servers at ECMWF to university clusters at Columbia University and Australian National University.

Applications and Use Cases

PRECIS has been applied to generate high‑resolution projections for national climate change communication, infrastructure planning, water resources management, and agricultural adaptation. Examples include downscaling global projections for drought and flood risk assessments used by World Bank financed projects, informing coastal vulnerability studies for UNESCO heritage sites, and supporting public health analyses by WHO on vector‑borne disease shifts. National meteorological services in countries such as Bangladesh, Ghana, Peru, Jamaica, and Fiji have employed PRECIS to produce scenario ensembles for integration into national adaptation plans submitted under UNFCCC processes and for sectoral planning by ministries of Transport, Agriculture, and Health.

Academic research groups at University of East Anglia, Imperial College London, Monash University, and University of Cape Town have used PRECIS outputs to drive hydrological models, crop models like those from CGIAR centres, and ecosystem impact assessments tied to Convention on Biological Diversity obligations.

Performance and Evaluation

Performance evaluations of PRECIS focus on skill in reproducing historical climatologies, sensitivity to boundary conditions provided by global models such as HadCM3 and GISS, and computational efficiency on available hardware. Validation studies comparing PRECIS hindcasts with observational networks maintained by WMO and reanalyses like ECMWF Reanalysis and NCEP/NCAR Reanalysis show improved representation of orographic rainfall, regional temperature gradients, and coastal circulations relative to coarse global fields, though results vary by domain and season. Intercomparison efforts with regional systems developed at UCAR and Institute Pierre Simon Laplace quantify uncertainties and guide ensemble construction for use in IPCC‑quality assessments.

Limitations and Criticisms

Criticisms of PRECIS include dependence on the quality of driving global model output (e.g., biases in CMIP ensembles), limited treatment of some convective and mesoscale processes relative to convection‑permitting approaches used in COST and newer convection‑resolving frameworks, and challenges in representing land‑use change dynamics relevant to IPCC impacts. Resource constraints in developing country institutions can limit capacity to run multi‑member ensembles and to perform comprehensive uncertainty analysis, leading to calls from groups like Green Climate Fund and UNDP for integrated training, greater transparency, and complementary use of statistical downscaling and high‑resolution dynamical models.

Category:Climate models