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Sea surface temperature (SST) changes in the Mediterranean Sea have profound impacts on both the Mediterranean regions and remote areas. Previous studies show that the Mediterranean SST has significant decadal variability that is comparable with the Atlantic multidecadal variability (AMV). However, few studies have discussed the characteristics and sources of the decadal predictability of Mediterranean SST based on observations. Here for the first time we use observational datasets to reveal that the decadal predictability of Mediterranean SST is contributed by both external forcings and internal variability for both annual and seasonal means, except that the decadal predictability of the winter mean SST in the eastern Mediterranean is mostly contributed by only internal variability. Besides, the persistence of the Mediterranean SST is quite significant even in contrast with that in the subpolar North Atlantic, which is widely regarded to have the most predictable surface temperature on the decadal time scale. After the impacts of external forcings are removed, the average prediction time of internally generated Mediterranean SST variations is more than 10 years and closely associated with the multidecadal variability of the Mediterranean SST that is closely related to the accumulated North Atlantic Oscillation forcing. Significance Statement Decadal prediction of sea surface temperature (SST) in the Mediterranean Sea is very important due to its profound climate impacts. Understanding the spatial/seasonal characteristics and sources of the decadal predictability of the Mediterranean SST are crucial for skillful decadal prediction of the Mediterranean SST and related variations. This study for the first time shows that the decadal predictability of the Mediterranean SST is quite significant even in comparison with the AMV. The decadal predictability of the Mediterranean SST is both region and season dependent. The internally generated decadal predictability of the Mediterranean SST is most probably related to the local ocean dynamics.

Hydrological sensitivity is the change in global-mean precipitation per degree of global-mean temperature change. This paper shows that the hydrological sensitivity of the response to anthropogenic aerosol forcing is distinct from that of the combined response to all other forcings and that this difference is sufficient to infer the associated cooling in global-mean temperature. This result is demonstrated using temperature and precipitation data generated by climate models and is robust across different climate models. Remarkably, greenhouse gas warming and aerosol cooling can be estimated in a model without using any spatial or temporal gradient information in the response, provided temperature data are augmented by precipitation data. Over the late twentieth century, the hydrological sensitivities of climate models differ significantly from that of observations. Whether this discrepancy can be attributed to observational error, which is substantial as different estimates of global-mean precipitation are not even significantly correlated with each other, or to model error is unclear. The results highlight the urgency to construct accurate estimates of global precipitation from past observations and for reducing model uncertainty in hydrological sensitivity. This paper also clarifies that previous estimates of hydrological sensitivity are limited in that standard regression methods neglect temperature–precipitation relations that occur through internal variability. An alternative method for estimating hydrological sensitivity that overcomes this limitation is presented.

This paper shows that joint temperature–precipitation information over a global domain provides a more accurate estimate of aerosol forced responses in climate models than does any other combination of temperature, precipitation, or sea level pressure. This fact is demonstrated using a new quantity called potential detectability, which measures the extent to which a forced response can be detected in a model. In particular, this measure can be evaluated independently of observations and therefore permits efficient exploration of a large number of variable combinations before performing optimal fingerprinting on observations. This paper also shows that the response to anthropogenic aerosol forcing can be separated from that of other forcings using only spatial structure alone, leaving the time variation of the response to be inferred from data, thereby demonstrating that temporal information is not necessary for detection. The spatial structure of the forced response is derived by maximizing the signal-to-noise ratio. For single variables, the north–south hemispheric gradient and equator-to-pole latitudinal gradient are important spatial structures for detecting anthropogenic aerosols in some models but not all. Sea level pressure is not an independent detection variable because it is derived partly from surface temperature. In no case does sea level pressure significantly enhance potential detectability beyond that already possible using surface temperature. Including seasonal or land–sea contrast information does not significantly enhance detectability of anthropogenic aerosol responses relative to annual means over global domains.

The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Niño–Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including 1) deriving a quantitative relation between predictability of the first and second kinds; 2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and 3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.

Long-term changes in land–atmosphere interactions during spring and summer are examined over North America. A suite of models from phase 5 of the Coupled Model Intercomparison Project simulating preindustrial, historical, and severe future climate change scenarios are examined for changes in soil moisture, surface fluxes, atmospheric boundary layer characteristics, and metrics of land–atmosphere coupling. Simulations of changes from preindustrial to modern conditions show warming brings stronger surface fluxes at high latitudes, while subtropical regions of North America respond with drier conditions. There is a clear anthropogenic aerosol response in midlatitudes that reduces surface radiation and heat fluxes, leading to shallower boundary layers and lower cloud base. Over the Great Plains, the signal does not reflect a purely radiatively forced response, showing evidence that the expansion of agriculture may have offset the aerosol impacts on the surface energy and water cycle. Future changes show soils are projected to dry across North America, even though precipitation increases north of a line that retreats poleward from spring to summer. Latent heat flux also has a north–south dipole of change, increasing north and decreasing south of a line that also moves northward with the changing season. Metrics of land–atmosphere feedback increase over most of the continent but are strongest where latent heat flux increases in the same location and season where precipitation decreases. Combined with broadly elevated cloud bases and deeper boundary layers, land–atmosphere interactions are projected to become more important in the future with possible consequences for seasonal climate prediction.