Coupled models tend to underestimate Indian summer monsoon (ISM) rainfall over most of the Indian subcontinent. Present study demonstrates that a part of dry bias is arising from the discrepancies in Oceanic Initial Conditions (OICs). Two hindcast experiments are carried out using Climate Forecast System (CFSv2) for summer monsoons of 2012–2014 in which two different OICs are utilized.

In ancient hothouses lacking ice sheets, the origins of large, million-year (myr)-scale sea-level oscillations remain a mystery, challenging current models of sea-level change. To address this mystery, we develop a sedimentary noise model for sea-level changes that simultaneously estimates geologic time and sea level from astronomically forced marginal marine stratigraphy. The noise model involves two complementary approaches: dynamic noise after orbital tuning (DYNOT) and lag-1 autocorrelation coefficient (ρ1).

We examine the capability of thirteen Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) models in simulating climatology and interannual variability of Winter North Pacific Storm Track (WNPST). It is found that nearly half of the selected models can reproduce the spatial pattern of WNPST climatology. However, the strength and spatial variation of WNPST climatology are weak in most of the models.

Original Source

The dynamic character of an enstrophy-based diagnostic, previously used in the study of atmospheric blocking, is examined here, in near-term future simulations from the Institut Pierre Simon Laplace Climate Model version 4 (IPSL-CM4) and version 5 (IPSL-CM5) climate models of the Northern Hemisphere flow for moderate climate change scenarios. Previous research has shown that integrated regional enstrophy (IE) increases during blocking onset and decay, which is a reflection of planetary-scale instability.

The social cost of carbon (SCC), a carbon price calculated from cost-benefit based integrated assessment models and used to inform some climate policies, will always be highly disputed, partly because a key model assumption, the centennial climate damage valuation function (CDF), will "always" be highly unknowable.