PapersFlow Research Brief
Climate variability and models
Research Guide
What is Climate variability and models?
Climate variability and models is the study of how and why the climate system varies across timescales and how numerical and data-assimilation models represent, attribute, and project those variations under natural and forced influences.
The research cluster on climate variability and models spans 152,431 works and centers on observing and simulating variability (e.g., ENSO, ocean circulation, hydrological-cycle variability) and extremes using reanalyses, gridded climatologies, and coupled climate models. Reanalysis products provide physically consistent, observation-constrained estimates of atmospheric and surface states, as described in "The NCEP/NCAR 40-Year Reanalysis Project" (1996), "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" (2011), and "The ERA5 global reanalysis" (2020). Model intercomparison frameworks standardize experiments and outputs for evaluating variability and change across ensembles, as laid out in "An Overview of CMIP5 and the Experiment Design" (2011) and "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016).
Topic Hierarchy
Research Sub-Topics
Climate Reanalysis Products
Researchers develop and validate global atmospheric reanalysis datasets like ERA5 and NCEP/NCAR by assimilating observations into models. Focus areas include uncertainty quantification and improvements in data assimilation techniques.
CMIP Coupled Model Intercomparison
This subfield involves designing and analyzing multi-model ensembles like CMIP5 and CMIP6 for climate projections. Studies assess model biases, emergent constraints, and scenario-based future warming estimates.
ENSO Variability and Predictability
Investigations examine El Niño-Southern Oscillation dynamics, teleconnections, and subseasonal predictability using observations and models. Research covers recharge mechanisms and impacts on global precipitation.
Arctic Amplification Mechanisms
Researchers study amplified Arctic warming through feedbacks like sea ice loss, lapse rate changes, and cloud alterations. Analysis includes observational trends and model simulations of polar climate dynamics.
Precipitation Extreme Events
This topic analyzes changes in heavy rainfall frequency, intensity, and spatial patterns under warming. Studies employ extreme value statistics, high-resolution modeling, and attribution to anthropogenic forcing.
Why It Matters
Climate variability and models matter because many decisions depend on quantified risks from climate variability and extremes, and those risks are estimated using standardized observational products and coordinated model experiments. For example, global reanalyses described in Kalnay et al. (1996) ("The NCEP/NCAR 40-Year Reanalysis Project"), Dee et al. (2011) ("The ERA‐Interim reanalysis: configuration and performance of the data assimilation system"), and Hersbach et al. (2020) ("The ERA5 global reanalysis") are widely used to characterize historical circulation patterns and event contexts in a dynamically consistent way. High-resolution gridded climate surfaces in Hijmans et al. (2005) ("Very high resolution interpolated climate surfaces for global land areas") and Fick and Hijmans (2017) ("WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas") support impact modeling that requires spatially complete temperature and precipitation fields at ~1 km land resolution. Sector-facing classification and baseline datasets—such as Peel et al. (2007) ("Updated world map of the Köppen-Geiger climate classification") for climate-zone mapping and Rayner et al. (2003) ("Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century") for sea-surface temperature and sea-ice analyses—provide inputs and evaluation targets for water resources, agriculture, ecosystem modeling, and coastal and marine planning workflows that depend on consistent historical baselines.
Reading Guide
Where to Start
Start with "An Overview of CMIP5 and the Experiment Design" (2011) because it explains, in a single place, how coordinated model experiments are structured and why multimodel archives are built for evaluating variability and change.
Key Papers Explained
A practical workflow often links observations, reanalyses, and model ensembles. Kalnay et al. (1996) in "The NCEP/NCAR 40-Year Reanalysis Project" established a long, consistent atmospheric record for monitoring and research, while Dee et al. (2011) in "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" documents a later ECMWF system and Hersbach et al. (2020) in "The ERA5 global reanalysis" describes the next-generation replacement extending back to 1950 onwards. For surface baselines used in impacts and regional analyses, Hijmans et al. (2005) in "Very high resolution interpolated climate surfaces for global land areas" and Fick and Hijmans (2017) in "WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas" provide gridded land climatologies at ~1 km resolution. For model evaluation across coordinated ensembles, Taylor et al. (2011) in "An Overview of CMIP5 and the Experiment Design" and Eyring et al. (2016) in "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" explain how standardized experiments enable systematic comparisons of simulated variability, while Rayner et al. (2003) in "Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century" supplies key ocean/ice historical analyses used to contextualize and evaluate coupled variability.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Advanced work typically focuses on reconciling differences among observationally constrained products and exploiting coordinated ensembles for variability attribution. One direction is careful cross-product comparison of inferred variability using the reanalysis lineage documented in "The NCEP/NCAR 40-Year Reanalysis Project" (1996), "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" (2011), and "The ERA5 global reanalysis" (2020). Another direction is using the CMIP program structure described in "An Overview of CMIP5 and the Experiment Design" (2011) and "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016) to design evaluation strategies that separate internal variability from forced responses, while maintaining consistent observational targets such as "Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century" (2003) and consistent land baselines such as "WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas" (2017).
Papers at a Glance
In the News
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Code & Tools
The Climate Variability Diagnostics Package (CVDP) developed by NCAR's Climate Analysis Section is an analysis tool that documents the major modes ...
ConceptualClimateModels.jl is a Julia package for creating and analysing conceptual models of climate, such as energy balance models, glaciation cy...
**ClimateLearn**is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way....
`climate4R` is a bundle of R packages for transparent climate data access, post-processing (including data collocation and bias correction / downsc...
A climate change scenario-building analysis framework, built with intake-esm catalogs and xarray-based packages such as xclim and xESMF. For docume...
Recent Preprints
Constraining climate model projections with observations ...
Climate models are increasingly used to inform water availability projections at regional scales. However, the models’ own runoff sensitivities—the change in runoff per unit change of precipitation...
new tools for the study of climate variability and change - GMD
Observations can be considered as one realisation of the climate system that we live in. To provide a fair comparison of climate models with observations, one must use multiple realisations or*ense...
Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models
Changes in temperature variability affect the frequency and intensity of extreme events, as well as the regional range of temperatures that ecosystems and society need to adapt to. While accurate p...
CMIP6 models cannot capture long-term forced changes in the tropical Pacific sea surface temperature gradient
The observed zonal sea surface temperature gradient in the tropical Pacific has strengthened over the last 150 years, but many CMIP6 models simulate a forced weakening of this gradient over the sam...
Climate Change, Variability and Prediction: Recent Publications
> Sea surface temperature (SST) variability on decadal timescales has been associated with global and regional climate variability and impacts. The mechanisms that drive decadal SST variability, ho...
Latest Developments
Recent research highlights from November 2025 indicate advances in understanding multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability, its interactions with Arctic salinity, and the role of Arctic salinity anomalies, as well as improved climate models that better capture long-term forced changes in sea surface temperatures and climate mode resonances, including the impacts of El Niño-Southern Oscillation (ENSO) and external forcing on Atlantic temperature and salinity (US CLIVAR, Nature Communications, Nature).
Sources
Frequently Asked Questions
What is the difference between a climate reanalysis and a climate model simulation for studying variability?
A reanalysis estimates the historical state of the atmosphere (and often land and ocean-wave variables) by combining observations with a fixed data assimilation system, as described in "The NCEP/NCAR 40-Year Reanalysis Project" (1996), "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" (2011), and "The ERA5 global reanalysis" (2020). A climate model simulation generates climate variability from the model’s equations and parameterizations under specified forcings and boundary conditions, and its behavior is often compared across coordinated experiments such as those in "An Overview of CMIP5 and the Experiment Design" (2011) and "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016).
How do CMIP5 and CMIP6 support research on climate variability and extremes?
"An Overview of CMIP5 and the Experiment Design" (2011) describes how CMIP5 coordinates a multimodel dataset so researchers can compare simulated variability and change under shared experimental protocols. "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016) explains how CMIP6 extends this coordination to address an expanding range of climate-science questions through organized experiments and shared data distribution.
Which datasets are commonly used to provide gridded climate baselines for variability and impact studies?
"Very high resolution interpolated climate surfaces for global land areas" (2005) provides interpolated global land climate surfaces at 30 arc s (often referred to as 1-km) resolution for monthly precipitation and temperature variables. "WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas" (2017) provides an updated ~1 km land dataset including monthly temperature and precipitation and additional variables such as solar radiation, vapour pressure, and wind speed.
How are sea surface temperature and sea ice observations incorporated into studies of climate variability?
"Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century" (2003) presents Hadley Centre datasets that combine information to produce monthly global analyses of sea surface temperature and sea ice. These products are commonly used as boundary conditions and evaluation targets when assessing simulated ocean-atmosphere variability in coupled models and reanalyses.
Which foundational modeling ideas underpin modern general circulation models used for variability studies?
"GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS" (1963) demonstrates numerical integration of a primitive-equation atmospheric model to simulate aspects of the general circulation. This lineage underlies modern general circulation models whose coupled configurations are then compared and evaluated through coordinated efforts described in "An Overview of CMIP5 and the Experiment Design" (2011) and "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016).
Which climate classification is often used to summarize spatial patterns relevant to variability and impacts?
"Updated world map of the Köppen-Geiger climate classification" (2007) provides an updated global Köppen–Geiger climate classification map that remains widely used in teaching and applied studies. Such classifications are often used to stratify analyses of variability, extremes, and impacts by climate regime using consistent global categories.
Open Research Questions
- ? How do differences in reanalysis system configuration and observing-system coverage influence inferred long-term variability when comparing "The NCEP/NCAR 40-Year Reanalysis Project" (1996), "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" (2011), and "The ERA5 global reanalysis" (2020)?
- ? Which aspects of simulated variability and extremes are most sensitive to coordinated experimental design choices described in "An Overview of CMIP5 and the Experiment Design" (2011) versus "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016)?
- ? How do uncertainties in gridded land-surface climatologies at ~1 km resolution—contrasting "Very high resolution interpolated climate surfaces for global land areas" (2005) and "WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas" (2017)—propagate into downstream detection of precipitation and temperature variability signals?
- ? To what extent do historical SST and sea-ice analyses in "Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century" (2003) constrain or bias assessments of coupled-model variability when used as evaluation targets or boundary conditions?
- ? How should climate-regime stratification using "Updated world map of the Köppen-Geiger climate classification" (2007) be integrated with reanalysis and CMIP evaluation to separate dynamical variability from thermodynamic shifts across regions?
Recent Trends
Across this topic area (152,431 works), widely cited infrastructure papers emphasize increasingly consistent, higher-quality reference datasets and more formally organized multimodel archives for evaluating variability.
The reanalysis progression from "The NCEP/NCAR 40-Year Reanalysis Project" to "The ERA‐Interim reanalysis: configuration and performance of the data assimilation system" (2011) and "The ERA5 global reanalysis" (2020) reflects a shift toward updated assimilation systems and extended temporal coverage (ERA5 described as extending from 1950 onwards).
1996In parallel, coordinated model evaluation has expanded from the CMIP5 design described in "An Overview of CMIP5 and the Experiment Design" to the broader CMIP6 organization described in "Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization" (2016), while high-resolution land baselines have been updated from "Very high resolution interpolated climate surfaces for global land areas" (2005) to "WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas" (2017).
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