Determination of eddy-diffusivity in the lowermost stratosphere

We present a 2D-advection-diffusion model that simulates the main transport pathways influencing tracer distributions in the lowermost stratosphere (LMS). The model describes slow diabatic descent of aged stratospheric air, vertical (cross-isentropic) and horizontal (along isentropes) diffusion within the LMS and across the tropopause using equivalent latitude and potential temperature coordinates. Eddy diffusion coefficients parameterize the integral effect of dynamical processes leading to small scale turbulence and mixing. They were specified by matching model simulations to observed CO distributions. Interestingly, the model suggests mixing across isentropes to be more important than horizontal mixing across surfaces of constant equivalent latitude, shining new light on the interplay between various transport mechanisms in the LMS. The model achieves a good description of the small scale tracer features at the tropopause with squared correlation coefficients R2 = 0.72…0.94.

Assessment of intercalibration methods for satellite microwave humidity sounders

Three methods for intercalibrating humidity sounding channels are compared to assess their merits and demerits. The methods use the following: (1) natural targets (Antarctica and tropical oceans), (2) zonal average brightness temperatures, and (3) simultaneous nadir overpasses (SNOs). Advanced Microwave Sounding Unit-B instruments onboard the polar-orbiting NOAA 15 and NOAA 16 satellites are used as examples. Antarctica is shown to be useful for identifying some of the instrument problems but less promising for intercalibrating humidity sounders due to the large diurnal variations there. Owing to smaller diurnal cycles over tropical oceans, these are found to be a good target for estimating intersatellite biases. Estimated biases are more resistant to diurnal differences when data from ascending and descending passes are combined. Biases estimated from zonal-averaged brightness temperatures show large seasonal and latitude dependence which could have resulted from diurnal cycle aliasing and scene-radiance dependence of the biases. This method may not be the best for channels with significant surface contributions. We have also tested the impact of clouds on the estimated biases and found that it is not significant, at least for tropical ocean estimates. Biases estimated from SNOs are the least influenced by diurnal cycle aliasing and cloud impacts. However, SNOs cover only relatively small part of the dynamic range of observed brightness temperatures.

Evidence of a universal scaling relationship for leaf CO2 drawdown along an aridity gradient

The leaf carbon isotope ratio (δ13C) of C3 plants is inversely related to the drawdown of CO2 concentration during photosynthesis, which increases towards drier environments. We aimed to discriminate between the hypothesis of universal scaling, which predicts between-species responses of δ13C to aridity similar to within-species responses, and biotic homoeostasis, which predicts offsets in the δ13C of species occupying adjacent ranges. The Northeast China Transect spans 130–900 mm annual precipitation within a narrow latitude and temperature range. Leaves of 171 species were sampled at 33 sites along the transect (18 at ≥ 5 sites) for dry matter, carbon (C) and nitrogen (N) content, specific leaf area (SLA) and δ13C. The δ13C of species generally followed a common relationship with the climatic moisture index (MI). Offsets between adjacent species were not observed. Trees and forbs diverged slightly at high MI. In C3 plants, δ13C predicted N per unit leaf area (Narea) better than MI. The δ13C of C4 plants was invariant with MI. SLA declined and Narea increased towards low MI in both C3 and C4 plants. The data are consistent with optimal stomatal regulation with respect to atmospheric dryness. They provide evidence for universal scaling of CO2 drawdown with aridity in C3 plants.

The phenology of phytoplankton blooms: ecosystems indicators from remote sensing

Remote sensing offers many advantages in the development of ecosystem indicators for the pelagic zone of the ocean. Particularly suitable in this context are the indicators arising from time series that can be constructed from remotely sensed data. For example, using ocean-colour radiometry, the phenology of phytoplankton blooms can be assessed. Metrics defined in this way show promise as informative indicators for the entire pelagic ecosystem. A simple phytoplankton–substrate model, with forcing dependent on latitude and day number is used to explore the qualitative features of bloom phenology for comparison with the results observed in a suite of 10-year time series of chlorophyll concentration, as assessed by remote sensing, from the Northwest Atlantic Ocean. The model reveals features of the dynamics that might otherwise have been overlooked in evaluation of the observational data.

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.

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: 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.

Estimation of sea surface temperature from the Spinning Enhanced Visible and Infra Red Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.

The influence of stratospheric vortex displacements and splits on surface climate

A strong link exists between stratospheric variability and anomalous weather patterns at the earth’s surface. Specifically, during extreme variability of the Arctic polar vortex termed a “weak vortex event,” anomalies can descend from the upper stratosphere to the surface on time scales of weeks. Subsequently the outbreak of cold-air events have been noted in high northern latitudes, as well as a quadrupole pattern in surface temperature over the Atlantic and western European sectors, but it is currently not understood why certain events descend to the surface while others do not. This study compares a new classification technique of weak vortex events, based on the distribution of potential vorticity, with that of an existing technique and demonstrates that the subdivision of such events into vortex displacements and vortex splits has important implications for tropospheric weather patterns on weekly to monthly time scales. Using reanalysis data it is found that vortex splitting events are correlated with surface weather and lead to positive temperature anomalies over eastern North America of more than 1.5 K, and negative anomalies over Eurasia of up to −3 K. Associated with this is an increase in high-latitude blocking in both the Atlantic and Pacific sectors and a decrease in European blocking. The corresponding signals are weaker during displacement events, although ultimately they are shown to be related to cold-air outbreaks over North America. Because of the importance of stratosphere–troposphere coupling for seasonal climate predictability, identifying the type of stratospheric variability in order to capture the correct surface response will be necessary.

The residual circulation of the Southern Ocean: which spatio-temporal scales are needed?

The Southern Ocean circulation consists of a complicated mixture of processes and phenomena that arise at different time and spatial scales which need to be parametrized in the state-of-the-art climate models. The temporal and spatial scales that give rise to the present-day residual mean circulation are here investigated by calculating the Meridional Overturning Circulation (MOC) in density coordinates from an eddy-permitting global model. The region sensitive to the temporal decomposition is located between 38°S and 63°S, associated with the eddy-induced transport. The ‘‘Bolus’’ component of the residual circulation corresponds to the eddy-induced transport. It is dominated by timescales between 1 month and 1 year. The temporal behavior of the transient eddies is examined in splitting the ‘‘Bolus’’ component into a ‘‘Seasonal’’, an ‘‘Eddy’’ and an ‘‘Inter-monthly’’ component, respectively representing the correlation between density and velocity fluctuations due to the average seasonal cycle, due to mesoscale eddies and due to large-scale motion on timescales longer than one month that is not due to the seasonal cycle. The ‘‘Seasonal’’ bolus cell is important at all latitudes near the surface. The ‘‘Eddy’’ bolus cell is dominant in the thermocline between 50°S and 35°S and over the whole ocean depth at the latitude of the Drake Passage. The ‘‘Inter-monthly’’ bolus cell is important in all density classes and is maximal in the Brazil–Malvinas Confluence and the Agulhas Return Current. The spatial decomposition indicates that a large part of the Eulerian mean circulation is recovered for spatial scales larger than 11.25°, implying that small-scale meanders in the Antarctic Circumpolar Current (ACC), near the Subantarctic and Polar Fronts, and near the Subtropical Front are important in the compensation of the Eulerian mean flow.

The vertical structure of ocean heat transport

One of the most important contributions the ocean makes to Earth's climate is through its poleward heat transport: about 1.5 PW or more than 30% of that accomplished by the ocean-atmosphere system (Trenberth and Caron, 2001). Recently, concern has arisen over whether global warming could affect this heat transport (Watson et al., 2001), for example, reducing high latitude convection and triggering a collapse of the deep overturning circulation (Rahmstorf, 1995). While the consequences of abrupt changes in oceanic circulation should be of concern, we argue that the attention devoted to deep circulations is disproportionate to their role in heat transport. For this purpose, we introduce a heat function which identifies the contribution to the heat transport by different components of the oceanic circulation. A new view of the ocean emerges in which a shallow surface intensified circulation dominates the poleward heat transport.

Comparison of D-region Doppler drift winds measured by the SuperDARN Finland HF radar over an annual cycle using the Kiruna VHF meteor radar

The SuperDARN chain of oblique HF radars has provided an opportunity to generate a unique climatology of horizontal winds near the mesopause at a number of high latitude locations, via the Doppler shifted echoes from sources of ionisation in the D-region. Ablating meteor trails form the bulk of these targets, but other phenomena also contribute to the observations. Due to the poor vertical resolution of the radars, care must be taken to reduce possible biases from sporadic-E layers and Polar Mesospheric Summer echoes that can affect the effective altitude of the geophysical parameters being observed. Second, there is strong theoretical and observational evidence to suggest that the radars are picking up echoes from the backward looking direction that will tend to reduce the measured wind strengths. The effect is strongly frequency dependent, resulting in a 20% reduction at 12 MHz and a 50% reduction at 10 MHz. A comparison of the climatologies observed by the Super-DARN Finland radar between September 1999 and September 2000 and that obtained from the adjacent VHF meteor radar located at Kiruna is also presented. The agreement between the two instruments was very good. Extending the analysis to the SuperDARN Iceland East radar indicated that the principles outlined above could be applied successfully to the rest of the SuperDARN network.

Using synoptic type analysis to understand New Zealand climate during the Mid-Holocene

Diagnosing the climate of New Zealand from low-resolution General Circulation Models (GCMs) is notoriously difficult due to the interaction of the complex topography and the Southern Hemisphere (SH) mid-latitude westerly winds. Therefore, methods of downscaling synoptic scale model data for New Zealand are useful to help understand past climate. New Zealand also has a wealth of palaeoclimate-proxy data to which the downscaled model output can be compared, and to provide a qualitative method of assessing the capability of GCMs to represent, in this case, the climate 6000 yr ago in the Mid-Holocene. In this paper, a synoptic weather and climate regime classification system using Empirical Orthogonal Function (EOF) analysis of GCM and reanalysis data was used. The climate regimes are associated with surface air temperature and precipitation anomalies over New Zealand. From the analysis in this study, we find at 6000 BP that increased trough activity in summer and autumn led to increased precipitation, with an increased north-south pressure gradient ("zonal events") in winter and spring leading to drier conditions. Opposing effects of increased (decreased) temperature are also seen in spring (autumn) in the South Island, which are associated with the increased zonal (trough) events; however, the circulation induced changes in temperature are likely to have been of secondary importance to the insolation induced changes. Evidence from the palaeoclimate-proxy data suggests that the Mid-Holocene was characterized by increased westerly wind events in New Zealand, which agrees with the preference for trough and zonal regimes in the models.