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The anthropogenic carbon distribution between the atmosphere, land surface and ocean varies significantly with the choice of scenario for identical changes in mean global surface temperature. Moving to a lower CO2 emissions scenario means that warming levels occur later, and with significantly less carbon in the three main carbon reservoirs. After 2 °C of warming, the multi-model mean ocean allocation can be up to 3 % different between scenarios, or 36 Pg in total with an even larger difference in some single model means. For the UKESM1 model, the difference between the minimum and maximum atmospheric fraction at the 2 °C Global Warming Level (GWL) is 3.6 %. This is equivalent to 50 Pg of additional carbon in the atmosphere, or the equivalent of five years of our current global total emissions. In the lower CO2 concentration scenarios, SSP1-1.9 and SSP1-2.6, the ocean fraction grows over time while the the land surface fraction remains constant. In the higher CO2 concentration scenarios, SSP2-4.5, SSP3-7.0 and SSP5-8.5, the ocean fraction remains constant over time while the the land surface fraction decreases over time. Higher equilibrium climate sensitivity (ECS) models reach the GWLs sooner, and with lower atmospheric CO2 than lower sensitivity models. However, the choice of scenario has a much larger impact on the percentage carbon allocation at a given warming level than the individual model's ECS.

Impulsive sounds generated during seismic surveys have elicited behavioral responses in marine mammals and could cause hearing impairment or injury. Mitigating exposure to seismic sound often relies on real-time marine mammal detection. Detection performance is influenced by detection method, environmental conditions, and observer experience. We conducted a field comparison of real-time detections made by marine mammal observers (MMOs), a rotating infrared (IR) camera, and via passive acoustic monitoring (PAM). Data were collected from a 38 m research vessel offshore Atlantic Canada. Our results indicate that overall detection rates increase when complementary methods are used. MMOs and PAM are likely the most effective combination during high seas and precipitation. PAM and IR can be used in darkness. In good visibility, MMOs with IR or PAM should increase detections. Our results illustrate the importance of addressing false positive IR detections, matching system capabilities to sea conditions/species of interest, and employing experienced observers.

Alces americanus (Moose) are relatively new to Labrador, having only colonized the area in the late 1940s, and little is known about this population. We conducted large-scale aerial surveys for Moose in a 122,000-km2 area during winter 2000 and in a 29,900-km2 area in winter 2001. Moose densities were low in each area (1.6–3.0 Moose per 100 km2). Bull:cow ratios were nearly even and calf:cow ratios were relatively high, indicative of a population exposed to little hunting or predation pressure. Twinning rates were low, suggesting low range productivity. Moose used riparian areas and hardwood stands in higher proportion than their availability in winter (P < 0.05). Open habitats (conifer-lichen woodlands, bogs and fens, burned forest, and barren areas) were used in lower proportion than their availability. These data may provide the basis for developing habitat suitability maps for Moose in late winter across central Labrador.

The Rossby Centre Atmospheric Regional Climate Model (RCA2) is described and simulation results, for the present climate over Europe, are evaluated against available observations. Systematic biases in the models mean climate and climate variability are documented and key parameterization weaknesses identified. The quality of near-surface parameters is investigated in some detail, particularly temperature, precipitation, the surface energy budget and cloud cover. The model simulates the recent, observed climate and variability with a high degree of realism. Compensating errors in the components of the surface radiation budget are highlighted and the fundamental causes of these biases are traced to the relevant aspects of the cloud, precipitation and radiation parameterizations. The model has a tendency to precipitate too frequently at small rates, this has a direct impact on the simulation of cloud-radiation interaction and surface temperatures. Great care must be taken in the use of observations to evaluate high resolution RCMs, when they are forced by analyzed boundary conditions. This is particularly true with respect to precipitation and cloudiness, where observational uncertainty is often larger than the RCM bias.

The Rossby Centre regional climate model (RCA2) has been integrated over the Arctic Ocean as part of the international ARCMIP project. Results have been compared to observations derived from the SHEBA data set. The standard RCA2 model overpredicts cloud cover and downwelling longwave radiation, during the Arctic winter. This error was improved by introducing a new cloud parameterization, which significantly improves the annual cycle of cloud cover. Compensating biases between clear sky downwelling longwave radiation and longwave radiation emitted from cloud base were identified. Modifications have been introduced to the model radiation scheme that more accurately treat solar radiation interaction with ice crystals. This leads to a more realistic representation of cloud-solar radiation interaction. The clear sky portion of the model radiation code transmits too much solar radiation through the atmosphere, producing a positive bias at the top of the frequent boundary layer clouds. A realistic treatment of the temporally evolving albedo, of both sea-ice and snow, appears crucial for an accurate simulation of the net surface energy budget. Likewise, inclusion of a prognostic snow-surface temperature seems necessary, to accurately simulate near-surface thermodynamic processes in the Arctic.

This paper describes a new 3.75° longitude by 2.5° latitude gridded monthly precipitation data series for the extended tropical region from 30°N to 30°S and covering the period 1974-1994. This new dataset combines the pre-existing land-based 3.75° x 2.5° resolution gridded monthly precipitation series of Hulme for the period 1974-1994, with a new monthly oceanic precipitation climatology, constructed from satellite outgoing longwave radiation (OLR) measurements also for the period 1974-1994. This latter dataset, which has been calibrated against available atoll raingauge observations, provides the longest record of satellite-derived precipitation estimates. A mean reliability index field to accompany this OLR-derived climatology is created from comparisons performed with two other satellite-derived precipitation datasets: the microwave sounding unit (MSU) precipitation dataset of Spencer and the preliminary global precipitation climatology project (GPCP) dataset developed at the Global Precipitation Climatology Centre. A Poisson blending technique is used to merge the land- and OLR-derived oceanic precipitation datasets to produce an extended tropical region climatology, suitable for global climate model validation and for other climatological analyses. This blended dataset shows good agreement with the GPCP product over the tropical region for the years 1986-1993.

Sea surface temperature (SST) anomalies associated with El Niño/Southern Oscillation (ENSO) constitute a major source of predictability in the tropics. We evaluate the ability of a regional climate model (the Rossby Centre Atmospheric Model; RCA) to downscale SST and large-scale atmospheric anomalies associated with ENSO. RCA is configured over the tropical east Pacific and tropical Americas and runs for the period 1979–2005, using European Centre for Medium-Range Weather Forecasts (ECMWF) lateral and surface boundary conditions. We study the ability of RCA to represent regional patterns of precipitation, with respect to both the climatology and interannual variability associated with ENSO. The latter is achieved by grouping the simulations into El Niño and La Niña composites and studying the delayed response of precipitation to SST forcing in four regions of Central and South America. In this paper, we concentrate on seasonal mean timescales. We find that RCA accurately simulates the main features of the precipitation climatology over the four regions and also reproduces the majority of the documented regional responses to ENSO forcing. Furthermore, the model captures the variability in precipitation anomalies between different ENSO events. The model exhibits a wet bias over the northern Amazon and slightly overestimates the magnitude of ENSO anomalies over Central America.

Using the Yearly Genesis Parameter (YGP) and the Convective-YGP (CYGP), the main large-scale climatic fields controlling tropical cyclone (TC) formation are analysed and used to infer the number of TCs in a given basin using ERA40 reanalyses for the period 1983–2002. Both indices show a reasonable global number and spatial distribution of implied TCs compared to observations. Using the same approach, we evaluate TC activity in the last 20-yr period of the 20th century in an ensemble of nine Coupled Global Climate Model simulations submitted to the IPCC AR4. We extend this analysis backwards in time, through the 20th century, and find the ensemble derived CYGP suggests no trend in inferred TC numbers while the YGP, after applying a correction to compensate for its oversensitivity to sea surface temperature, suggests a small upward trend. Both indices give a fair geographical distribution of cyclogenesis. Finally, we assess future TC trends using three emission scenarios. Using the CYGP, which appears the most robust index for application to climate change, a small increase is predicted in the northwestern Pacific in the A1B and A2 scenarios.