Extract hindcasts for a fitted mvgam object

Hurricane Katrina hindcasts are evaluated for the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) v1 and the Weather Research and Forecasting (WRF) model SCREAMv1 at 3.25 km simulated a rea...

Abstract. Simple climate models (also known as emulators) have re-emerged as critical tools for analysis of climate policy. Emulators are efficient and highly parameterised, where the parameters are tunable to produce a diversity of global mean surface temperature (GMST) response pathways to a given emissions scenario. Only a small fraction of possible parameter combinations will produce historically consistent climate hindcasts, a necessary condition for trust in future projections. Alongside historical GMST, additional observed (e.g. ocean heat content) and emergent climate metrics (such as the equilibrium climate sensitivity) can be used as constraints upon the parameter sets used for climate projections. This paper describes a multi-variable constraining package for the FaIR simple climate model (FaIR versions 2.1.0 onwards) using a Bayesian framework. The steps are firstly to generate prior distributions of parameters for FaIR based on Coupled Model Intercomparison Project (CMIP6) Earth System models or Intergovernmental Panel on Climate Change (IPCC) assessed ranges, secondly to generate a large Monte Carlo prior ensemble of parameters to run FaIR with, and thirdly to produce a posterior set of parameters constrained on several observable and assessed climate metrics. Different calibrations can be produced for different emissions datasets or observed climate constraints, allowing version-controlled and continually updated calibrations to be produced. We show that two very different future projections to a given emission scenario can be obtained using emissions from the IPCC Sixth Assessment Report (AR6) (fair-calibrate v1.4.0) and from updated emissions datasets through 2022 (fair-calibrate v1.4.1) for similar climate constraints in both cases. fair-calibrate can be reconfigured for different source emissions datasets or target climate distributions, and new versions will be produced upon availability of new climate system data.

As part of the Changing Ecosystems and Fisheries Initiative, NOAA researchers have developed a new model that helps us to better understand past ocean conditions so we can predict future ones with gre...

The skill of predicting the spatial pattern of ENSO events in coupled seasonal hindcasts is low in 1930–1960, compared to before and after The skill minimum is attributable to lower ENSO variabil...

We demonstrate that fossil-informed SDMs not only improve spatial accuracy and ecological realism in past projections, but also provide an external form of validation that traditional hindcasts often...

We analyse the Northern Annular Mode (NAM) in hindcasts from nine seasonal prediction models. In all the models, the Pacific centre-of-action is much larger than in reanalysis. We show that this disc....

Seasonal crop yield forecasts can support early warning and short-term adaptation in sub-Saharan Africa (SSA), where rainfed agriculture dominates, but systematic multi-crop, multi-region evaluations remain scarce. Here, we develop and evaluate a forecasting system in which bias-corrected, downscaled SEAS5 forecasts drive the LPJmL crop model. We generated 30 year hindcasts (1994–2023) for all major crops in Ghana, Kenya, and Zimbabwe, three contrasting SSA agro-climatic regimes, and assessed probabilistic accuracy and event discrimination from four months before to six months after sowing, against a climatological reference from an ERA5-Land-driven simulation. Probabilistic skill (continuous ranked probability skill score) was weak or negative around sowing and improved for later initialisations, most consistently in Zimbabwe, where the country mean rose from about 0.08 at sowing to about 0.7 four months later. Kenya improved similarly but with greater spatial heterogeneity, partly reflecting limited climate-forecast skill for its long rains, while Ghana showed predominantly negative early-season skill. All crop–country pairs reached positive skill in at least one metric by one month before harvest. Event discrimination was more informative than probabilistic accuracy at long lead times, separating above- from below-normal yield years for most crop–country pairs even before sowing. For post-sowing initialisations, area under the relative operating characteristic curve exceeded 0.7 for about 80% of grid cells on average, improving on recent climatology by up to about 0.5. The forecasts can therefore signal the direction of simulated yield anomalies well before their magnitude becomes predictable. This skill reflects the predictability of climate-driven yield variability as simulated by LPJmL. However, agreement between simulated and reported yield anomalies varied by crop and country, and was highest for maize in Zimbabwe and lowest in Ghana.

This study assesses MPI-ESM 1.2-LR decadal hindcasts (lead year 1-5) to evaluate the prediction of European compound hot and dry summers with help of North Atlantic Sea Surface Temperature (SST). We e...