A two-tiered approach to long-range climate forecasting

Long-range global climate forecasts were made by use of a model for predicting a tropical Pacific sea-surface temperature (SST) in tandem with an atmospheric general circulation model. The SST is predicted first at long lead times into the future. These ocean forecasts are then used to force the atmospheric model and so produce climate forecasts at lead times of the SST forecasts. Prediction of seven large climatic events of the 1970s to 1990s by this technique are in good agreement with observations over many regions of the globe.

Have aerosols caused the observed Atlantic multidecadal variability?

Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently, Booth et al. showed that the Hadley Centre Global Environmental Model, version 2, Earth system configuration (HadGEM2-ES) closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the twentieth century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al. concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper-ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anticorrelation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.

Changes in tropical Atlantic interannual variability from a substantial weakening of the meridional overturning circulation

In response to a substantial weakening of the Atlantic Meridional Overturning Circulation (AMOC)— from a coupled ocean–atmosphere general circulation model experiment—significant changes in the interannual variability are found over the tropical Atlantic, characterized by an increase of variance (by ~150 %) in boreal late spring-early summer and a decrease of variance (by ~60 %) in boreal autumn. This study focuses on understanding physical mechanisms responsible for these changes in interannual variability in the tropical Atlantic. It demonstrates that the increase of variability in spring is a consequence of an increase in the variance of the El Niño-Southern Oscillation, which has a large impact on the tropical Atlantic via anomalous surface heat fluxes. Winter El Niño (La Niña) affects the eastern equatorial Atlantic by decreasing (increasing) cloud cover and surface wind speed which is associated with anomalous downward (upward) short wave radiation and reduced (enhanced) upward latent heat fluxes, creating anomalous positive (negative) sea surface temperature (SST) anomalies over the region from winter to spring. On the other hand, the decrease of SST variance in autumn is due to a deeper mean thermocline which weakens the impact of the thermocline movement on SST variation. The comparison between the model results and observations is not straightforward owing to the influence of model biases and the lack of a major MOC weakening event in the instrumental record. However, it is argued that the basic physical mechanisms found in the model simulations are likely to be robust and therefore have relevance to understanding tropical Atlantic variability in the real world, perhaps with modified seasonality.

The Greenland Ice Sheet's surface mass balance in a seasonally sea-ice free Arctic

General circulation models predict a rapid decrease in sea ice extent with concurrent increases in near surface air temperature and precipitation in the Arctic over the 21st century. This has led to suggestions that some Arctic land ice masses may experience an increase in accumulation due to enhanced evaporation from a seasonally sea ice free Arctic Ocean. To investigate the impact of this phenomenon on Greenland ice sheet climate and surface mass balance (SMB) a regional climate model, HadRM3, was used to force an insolation-temperature melt SMB model. A set of experiments designed to investigate the role of sea ice independently from sea surface temperature (SST) forcing are described. In the warmer and wetter SI + SST simulation Greenland experiences a 23% increase in winter SMB but 65% reduced summer SMB, resulting in a net decrease in the annual value. This study shows that sea ice decline contributes to the increased winter balance, causing 25% of the increase in winter accumulation; this is largest in eastern Greenland as the result of increased evaporation in the Greenland Sea. These results indicate that the seasonal cycle of Greenland's SMB will increase dramatically as global temperatures increase, with the largest changes in temperature and precipitation occurring in winter. This demonstrates that the accurate prediction of changes in sea ice cover is important for predicting Greenland SMB and ice sheet evolution.

Interhemispheric coupling, the West Antarctic Ice Sheet and warm Antarctic interglacials

Ice core evidence indicates that even though atmospheric CO2 concentrations did not exceed ~300 ppm at any point during the last 800 000 years, East Antarctica was at least ~3–4 °C warmer than preindustrial (CO2~280 ppm) in each of the last four interglacials. During the previous three interglacials, this anomalous warming was short lived (~3000 years) and apparently occurred before the completion of Northern Hemisphere deglaciation. Hereafter, we refer to these periods as "Warmer than Present Transients" (WPTs). We present a series of experiments to investigate the impact of deglacial meltwater on the Atlantic Meridional Overturning Circulation (AMOC) and Antarctic temperature. It is well known that a slowed AMOC would increase southern sea surface temperature (SST) through the bipolar seesaw and observational data suggests that the AMOC remained weak throughout the terminations preceding WPTs, strengthening rapidly at a time which coincides closely with peak Antarctic temperature. We present two 800 kyr transient simulations using the Intermediate Complexity model GENIE-1 which demonstrate that meltwater forcing generates transient southern warming that is consistent with the timing of WPTs, but is not sufficient (in this single parameterisation) to reproduce the magnitude of observed warmth. In order to investigate model and boundary condition uncertainty, we present three ensembles of transient GENIE-1 simulations across Termination II (135 000 to 124 000 BP) and three snapshot HadCM3 simulations at 130 000 BP. Only with consideration of the possible feedback of West Antarctic Ice Sheet (WAIS) retreat does it become possible to simulate the magnitude of observed warming.

Predictable climate impacts of the decadal changes in the ocean in the 1990s

During the 1990s there was a major change in the state of the world's oceans. In particular, the North Atlantic underwent a rapid warming, with sea surface temperatures (SSTs) in the subpolar gyre region increasing by 1°C in just a few years. Associated with the changes in SST patterns were changes in the surface climate, in particular, a tendency for warm and dry conditions over areas of North America in all seasons, and warm springs and wet summers over areas of Europe. Here, the extent to which a climate prediction system initialized using observations of the ocean state is able to capture the observed changes in seasonal mean surface climate is investigated. Rather than examining predictions of the mid-1990s North Atlantic warming event itself, this study compares hindcasts started before and after the warming, relative to hindcasts that do not assimilate information. It is demonstrated that the hindcasts capture many aspects of the observed changes in seasonal mean surface climate, especially in North, South, and Central America and in Europe. Furthermore, the prediction system retains skill beyond the first year. Finally, it is shown that, in addition to memory of Atlantic SSTs, successfully predicting Pacific SSTs was likely important for the hindcasts to predict surface climate over North America.

A twenty-year independent record of sea surface temperature for climate from Along-track Scanning Radiometers

A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along-Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude-longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics-based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three-way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr−1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ-based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.

Diurnal warm-layer events in the western Mediterranean and European shelf seas

We characterize near-surface ocean diurnal warm-layer events, using satellite observations and fields from numerical weather forecasting. The study covers April to September, 2006, over the area 11°W to 17°E and 35°N to 57°N, with 0.1° cells. We use hourly satellite SSTs from which peak amplitudes of diurnal cycles in SST (dSSTs) can be estimated with error ∼0.3 K. The diurnal excursions of SST observed are spatially and temporally coherent. The largest dSSTs exceed 6 K, affect 0.01% of the surface, and are seen in the Mediterranean, North and Irish Seas. There is an anti-correlation between the magnitude and the horizontal length scale of dSST events. Events wherein dSST exceeds 4 K have length scales of ≤40 km. From the frequency distribution of different measures of wind-speed minima, we infer that extreme dSST maxima arise where conditions of low wind speed are sustained from early morning to mid afternoon.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7 μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0 μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST. In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39 K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer

Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should be close to 1 K K−1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High Resolution Radiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from −0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between −0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by <0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically.

A statistical model for sea surface diurnal warming driven by numercial weather predictions fluxes and winds

A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: initial validation, accounting for skin and diurnal variability

An initial validation of the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate (ARC) retrievals of sea surface temperature (SST) is presented. ATSR-2 and Advanced ATSR (AATSR) SST estimates are compared to drifting buoy and moored buoy observations over the period 1995 to 2008. The primary ATSR estimates are of skin SST, whereas buoys measure SST below the surface. Adjustment is therefore made for the skin effect, for diurnal stratification and for differences in buoy–satellite observation time. With such adjustments, satellite-in situ differences are consistent between day and night within ~ 0.01 K. Satellite-in situ differences are correlated with differences in observation time, because of the diurnal warming and cooling of the ocean. The data are used to verify the average behaviour of physical and empirical models of the warming/cooling rates. Systematic differences between adjusted AATSR and in-situ SSTs against latitude, total column water vapour (TCWV), and wind speed are less than 0.1 K, for all except the most extreme cases (TCWV < 5 kg m–2, TCWV > 60 kg m–2). For all types of retrieval except the nadir-only two-channel (N2), regional biases are less than 0.1 K for 80% of the ocean. Global comparison against drifting buoys shows night time dual-view two-channel (D2) SSTs are warm by 0.06 ± 0.23 K and dual-view three-channel (D3) SSTs are warm by 0.06 ± 0.21 K (day-time D2: 0.07 ± 0.23 K). Nadir-only results are N2: 0.03 ± 0.33 K and N3: 0.03 ± 0.19 K showing the improved inter-algorithm consistency to ~ 0.02 K. This represents a marked improvement from the existing operational retrieval algorithms for which inter-algorithm inconsistency is > 0.5 K. Comparison against tropical moored buoys, which are more accurate than drifting buoys, gives lower error estimates (N3: 0.02 ± 0.13 K, D2: 0.03 ± 0.18 K). Comparable results are obtained for ATSR-2, except that the ATSR-2 SSTs are around 0.1 K warm compared to AATSR

A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: a new retrieval scheme

We present a new coefficient-based retrieval scheme for estimation of sea surface temperature (SST) from the Along Track Scanning Radiometer (ATSR) instruments. The new coefficients are banded by total column water vapour (TCWV), obtained from numerical weather prediction analyses. TCWV banding reduces simulated regional retrieval biases to < 0.1 K compared to biases ~ 0.2 K for global coefficients. Further, detailed treatment of the instrumental viewing geometry reduces simulated view-angle related biases from ~ 0.1 K down to < 0.005 K for dual-view retrievals using channels at 11 and 12 μm. A novel analysis of trade-offs related to the assumed noise level when defining coefficients is undertaken, and we conclude that adding a small nominal level of noise (0.01 K) is optimal for our purposes. When applied to ATSR observations, some inter-algorithm biases appear as TCWV-related differences in SSTs estimated from different channel combinations. The final step in coefficient determination is to adjust the offset coefficient in each TCWV band to match results from a reference algorithm. This reference uses the dual-view observations of 3.7 and 11 μm. The adjustment is independent of in situ measurements, preserving independence of the retrievals. The choice of reference is partly motivated by uncertainty in the calibration of the 12 μm of Advanced ATSR. Lastly, we model the sensitivities of the new retrievals to changes to TCWV and changes in true SST, confirming that dual-view SSTs are most appropriate for climatological applications

A reprocessing for climate of sea surface temperature from the Along-Track Scanning Radiometers: basis in radiative transfer

We present new radiative transfer simulations to support determination of sea surface temperature (SST) from Along Track Scanning Radiometer (ATSR) imagery. The simulations are to be used within the ATSR Reprocessing for Climate project. The simulations are based on the “Reference Forward Model” line-by-line model linked with a sea surface emissivity model that accounts for wind speed and temperature, and with a discrete ordinates scattering model (DISORT). Input to the forward model is a revised atmospheric profile dataset, based on full resolution ERA-40, with a wider range of high-latitude profiles to address known retrieval biases in those regions. Analysis of the radiative impacts of atmospheric trace gases shows that geographical and temporal variation of N2O, CH4, HNO3, and CFC-11 and CFC-12 have effects of order 0.05, 0.2, 0.1 K on the 3.7, 11, 12 μm channels respectively. In addition several trace gases, neglected in previous studies, are included using fixed profiles contributing ~ 0.04 K to top-of-atmosphere BTs. Comparison against observations for ATSR2 and AATSR indicates that forward model biases have been reduced from 0.2 to 0.5 K for previous simulations to ~ 0.1 K.

An infrared desert dust index for the Along-Track Scanning Radiometers

A new aerosol index for the Along-Track Scanning Radiometers (ATSRs) is presented that provides a means to detect desert dust contamination in infrared SST retrievals. The ATSR Saharan dust index (ASDI) utilises only the thermal infrared channels and may therefore be applied consistently to the entire ATSR data record (1991 to present), for both day time and night time observations. The derivation of the ASDI is based on a principal component (PC) analysis (PCA) of two unique pairs of channel brightness temperature differences (BTDs). In 2-D space (i.e. BTD vs BTD), it is found that the loci of data unaffected by aerosol are confined to a single axis of variability. In contrast, the loci of aerosol-contaminated data fall off-axis, shifting in a direction that is approximately orthogonal to the clear-sky axis. The ASDI is therefore defined to be the second PC, where the first PC accounts for the clear-sky variability. The primary ASDI utilises the ATSR nadir and forward-view observations at 11 and 12 μm (ASDI2). A secondary, three-channel nadir-only ASDI (ASDI3) is also defined for situations where data from the forward view are not available. Empirical and theoretical analyses suggest that ASDI is well correlated with aerosol optical depth (AOD: correlation r is typically > 0.7) and provides an effective tool for detecting desert mineral dust. Overall, ASDI2 is found to be more effective than ASDI3, with the latter being sensitive only to very high dust loading. In addition, use of ASDI3 is confined to night time observations as it relies on data from the 3.7 μm channel, which is sensitive to reflected solar radiation. This highlights the benefits of having data from both a nadir- and a forward-view for this particular approach to aerosol detection.