Summary for policymakers Technical summary 1. The climate system - an overview 2. Observed climate variability and change 3. The carbon cycle and atmospheric CO2 4. Atmospheric chemistry and greenhouse gases 5. Aerosols, their direct and indirect effects 6. Radiative forcing of climate change 7. Physical climate processes and feedbacks 8. Model evaluation 9. Projections of future climate change 10. Regional climate simulation - evaluation and projections 11. Changes in sea level 12. Detection of climate change and attribution of causes 13. Climate scenario development 14. Advancing our understanding Glossary Index Appendix.

Climate change 2001: the scientific basis

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model-simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000JD900719

Summarizing multiple aspects of model performance in a single diagram

The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

NCEP–DOE AMIP-II Reanalysis (R-2)

Changes in Atmospheric Constituents and in Radiative Forcing

Verification of S-Polka Ka band radar/radiometer LWC and RES retrievals with GRIDS retrievals and aircraft measurements and comparison to GOES icing products for the WISP04 10-11 March event [presentation]

An orbital analysis was performed to evaluate the effects of orbit altitude and equatorial crossing time on meeting the scientific requirements for the various experiments on the Earth Observing System (EOS) satellite. Results show that a 705-km orbit is more advantageous than an 824-km orbit for EOS. The lower orbit has higher spatial resolution and provides global coverage in a shorter period of time with a reasonable scan angle than the 824-km orbit. Both late-morning and early-afternoon equatorial crossing times offer certain advantages depending on the specific EOS science objective. Multiple satellites with equatorial crossing times spaced throughout the day are required to study diurnal variations of clouds, winds, and radiative parameters.

Orbital analysis and instrument viewing considerations for the Earth Observing System (EOS) satellite

Evaluation of CERES-MODIS Ed4 Cloud Macrophysical Properties using 11-yr ARM Ground-based Observations over the Arctic.

This paper reports the use of a specialized, mesoscale, numerical weather prediction (NWP) system and a satellite imaging and prediction system that were set up to support the CLAMS (Chesapeake Lighthouse and Aircraft Measurements for Satellites) field campaign during the summer of 2001. The primary objective of CLAMS was to validate satellite-based retrievals of aerosol properties and vertical profiles of the radiative flux, temperature and water vapor. Six research aircraft were deployed to make detailed coincident measurements of the atmosphere and ocean surface with the research satellites that orbited overhead. The mesoscale weather modeling system runs in real-time to provide high spatial and temporal resolution for forecasts that are delivered via the World Wide Web along with a variety of satellite imagery and satellite location predictions. This system is a multi-purpose modeling system capable of both data analysis/assimilation and multi-scale NWP ranging from cloud-scale to larger than regional scale. This is a three-dimensional, nonhydrostatic compressible model in a terrain-following coordinate. The model employs advanced numerical techniques and contains detailed interactive physical processes. The utility of the forecasting system is illustrated throughout the discussion on the impact of the surface-wind forecast on BRDF (Bidirectional Reflectance Distribution Function) and the description of the cloud/moisture forecast versus the aircraft measurement.

Real-Time Mesoscale Forecast Support During the CLAMS Field Campaign

Aerosols that serve as cloud condensation nuclei (CCN) and ice nuclei (IN) have the potential to profoundly influence precipitation processes. Furthermore, changes in orographic precipitation have broad implications for reservoir storage and flood risks. As part of the CalWater I field campaign (2009-2011), the impacts of aerosol sources on precipitation were investigated in the California Sierra Nevada. In 2009, the precipitation collected on the ground was influenced by both local biomass burning (up to 79% of the insoluble residues found in precipitation) and long-range transported dust and biological particles (up to 80% combined), while in 2010, by mostly local sources of biomass burning and pollution (30-79% combined), and in 2011 by mostly long-range transport from distant sources (up to 100% dust and biological). Although vast differences in the source of residues was observed from year-to-year, dust and biological residues were omnipresent (on average, 55% of the total residues combined) and were associated with storms consisting of deep convective cloud systems and larger quantities of precipitation initiated in the ice phase. Further, biological residues were dominant during storms with relatively warm cloud temperatures (up to -15 C), suggesting these particles were more efficient IN compared to mineral dust. On the other hand, lower percentages of residues from local biomass burning and pollution were observed (on average 31% and 9%, respectively), yet these residues potentially served as CCN at the base of shallow cloud systems when precipitation quantities were low. The direct connection of the source of aerosols within clouds and precipitation type and quantity can be used in models to better assess how local emissions versus long-range transported dust and biological aerosols play a role in impacting regional weather and climate, ultimately with the goal of more accurate predictive weather forecast models and water resource management.

/pdf/interannual-variations-in-aerosol-sources-and-their-impact-16rt0oly1p.pdf

Patrick Minnis

Papers

Verification of S-Polka Ka band radar/radiometer LWC and RES retrievals with GRIDS retrievals and aircraft measurements and comparison to GOES icing products for the WISP04 10-11 March event [presentation]

Orbital analysis and instrument viewing considerations for the Earth Observing System (EOS) satellite

Evaluation of CERES-MODIS Ed4 Cloud Macrophysical Properties using 11-yr ARM Ground-based Observations over the Arctic.

Real-Time Mesoscale Forecast Support During the CLAMS Field Campaign

Interannual Variations in Aerosol Sources and Their Impact on Orographic Precipitation Over California's Central Sierra Nevada