Reanalysis Temperature Research Articles

The role of the Mt. Merapi eruption in the 2011 Arctic ozone depletion

One of the strongest ozone depletion events in the Arctic stratosphere was observed in March 2011 due to the strengthening of the polar vortex in February 2011. Earlier, in November 2010, the eruption of Mt. Merapi volcano (Java, Indonesia) with a maximum plume altitude of 18.3 km was recorded. The effect of aerosol heating in the tropical lower stratosphere after the Mt. Merapi eruption on the Arctic polar vortex strengthening in winter–spring 2011 is examined. Based on the ERA-Interim reanalysis temperature data, we show that significant aerosol heating in the lower tropical stratosphere was observed in February–March 2011 and could lead to an increase in the stratospheric equator-to-pole temperature gradient resulting in an enhanced Arctic polar vortex. We also analyze the correlation between large tropical volcanic eruptions occurring in autumn–winter periods and Arctic ozone depletion events observed in the following winter–spring periods.

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

ABSTRACTIn the present study, the performance of the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP‐II) reanalysis (NCEP‐2) and NCEP Climate Forecast System Reanalysis (CFSR) seasonal temperature data for China was quantitatively evaluated by using detrended fluctuation analysis. The results indicate that the quality of the NCEP‐2 temperature data is the highest in autumn, while the quality of CFSR data set is the highest in summer. Both the quality of NCEP‐2 and that of CFSR temperature data is the lowest in winter. The quality is very low in eastern Tibetan Plateau and most of Xinjiang at a significance level of 0.05 for the NCEP‐2 and CFSR reanalysis temperature data including three elements: the daily average temperature (Tmean), the daily maximum temperature (Tmax) and the daily minimum temperature (Tmin). The performance of the NCEP‐2 Tmean data is poorer than those of the NCEP‐2 Tmax and Tmin data for all four seasons, while the performance of CFSR Tmean is better than that of CFSR Tmax and Tmin for all four seasons. In addition, the NCEP‐2 Tmax, Tmin and CFSR Tmean, Tmax data perform well in most of central and eastern China for all four seasons. The quality of the CFSR Tmean is better than that of the NCEP‐2 Tmean for all four seasons. However, the NCEP‐2 Tmax and Tmin data have higher credibility than that of the CFSR Tmax and Tmin data for all four seasons. Therefore, it is important to consider the reliability of the NCEP‐2 and CFSR daily temperature reanalysis data in different districts and seasons when drawing conclusions from the different reanalysis data.

Modeling of the middle atmosphere response to 27-day solar irradiance variability

The solar rotational variability (27-day) signal in the Earth's middle atmosphere has been studied for several decades, as it was believed to help in the understanding of the Sun's influence on climate at longer timescales. However, all previous studies have found that this signal is very uncertain, likely due to the influence of the internal variability of the atmosphere. Here, we applied an ensemble modeling approach in order to decrease internal random variations in the modeled time series. Using a chemistry-climate model (CCM), SOCOLv3, we performed two 30-member 3-year long (2003–2005) ensemble runs: with and without a rotational component in input irradiance fluxes. We also performed similar simulations with a 1-D model, in order to demonstrate the system behavior in the absence of any dynamical feedbacks and internal perturbations. For the first time we show a clear connection between the solar rotation and the stratospheric tropical temperature time-series. We show tropical temperature and ozone signal phase lag patterns that are in agreement with those from a 1-D model. Pronounced correlation and signal phase lag patterns allow us to properly estimate ozone and temperature sensitivities to irradiance changes. While ozone sensitivity is found to be in agreement with recent sensitivities reported for the 11-year cycle, temperature sensitivity appears to be at the lowest boundary of previously reported values. Analysis of temperature reanalysis data, separate ensemble members, and modeling results without a rotational component reveals that the atmosphere can produce random internal variations with periods close to 27 days even without solar rotational forcing. These variations are likely related to tropospheric wave-forcing and complicate the extraction of the solar rotational signal from observational time-series of temperature and, to a lesser extent, of ozone. Possible ways of further improving solar rotational signal extraction are discussed.

Elevation correction of ERA‐Interim temperature data in the Tibetan Plateau

ABSTRACTThe near surface air temperature is the primary indicator for climate change. Reanalysis as the surrogates for large‐scale observations are widely used in the Tibetan Plateau because of the sparse meteorological network. However, an average bias of −3.54 °C and root‐mean‐square error (RMSE) of 4.31 °C were found between ERA‐Interim monthly 2‐m temperature and observation over the Tibetan Plateau, which indicated that a correction procedure for ERA‐Interim is necessary before local scale applications. To overcome this challenge, a robust elevation correction method is developed to downscale ERA‐Interim 2° × 2° monthly 2‐m temperature data based on ERA‐Interim internal vertical lapse rates. This method is validated against 80 meteorological stations from 1979 to 2013 located in 26 ERA‐Interim grid cells. It is also compared with other four correction methods, which are using different lapse rate schemes such as fixed monthly lapse rates, surface lapse rates calculated from the meteorological stations (within a single grid or with neighbouring sites), as well as a third‐order curvilinear function of ERA‐Interim pressure level data. The results indicate that the correction method using ERA‐Interim internal vertical lapse rates cannot only significantly reduce the bias (89%) and RMSE (62%) for the original ERA‐Interim data, but also capture the inter‐annual variations for the plateau‐wide climatology very well. The seasonal and annual temperature warming trends are also modelled encouragingly compared with other four methods. The strongest advantage of this method is that it is independent of local meteorological stations. Therefore, it is possible to extrapolate ERA‐Interim temperature data for any other high mountain areas where no measurements exist. This work will help the scientific community identify the most proper and easiest method to downscale reanalysis temperature data for climate impact assessments at the site or regional scale.

Extension of physical scaling method and its application towards downscaling climate model based near surface air temperature

ABSTRACTPhysical scaling (SP) method is a statistical downscaling approach where model‐based climate data are downscaled taking into consideration large‐scale climate, elevation and land‐cover at the location of interest. In this study, the downscaling skills of an ensemble of SP method and its variants and Statistical DownScaling Model (SDSM) towards downscaling North American Regional Reanalysis (NARR) temperature data are compared. Two downscaling approaches: direct and indirect, two versions: SP and surrounding pixel information and three functional forms: linear regression, quantile regression and generalized additive models are considered to prepare the method ensemble. To evaluate method performance, a leave‐one‐out cross‐validation approach is adopted. Results indicate that SP method and its variants have comparable skill to SDSM. Further method skill is found to be only marginally influenced by the choice of method version and functional form, and considerably influenced by the choice of approach. The ensemble of models is thereafter used to downscale future air temperature projections made by a global climate model: FGOALS‐s2. It is found that downscaled future projections are most significantly influenced by the choice of version, followed by the choice of approach and the choice of functional form in the decreasing order of importance.

Studies on changes and cause of the minimum air temperature in Songnen Plain of China during 1961–2010

In this study, we took Songnen Plain of China as the study area, collected the observed minimum air temperature, the minimum air temperature reanalysis data (ERA-40), and the minimum land surface temperature from 1961 to 2010, the nighttime land surface temperature (MOD11A2) from 2000 to 2010. Further, with the obtained and processed data, we analyzed the mechanism and impact of land use change on minimum air temperature within the recent 50years in the Songnen Plain of China using climate tendency rate, Observation Minus Reanalysis (OMR) and spatial analysis methods. The analysis results can be summarized as follows. (1) The areas of agricultural land, built up, unused land, and forest land have increased and the areas of grassland and water have decreased from 1960 to 2010. In particular, the areas of unused land and built up have increased significantly with about 22 times and 9 times respectively compared with their areas in 1960. (2) During 1961–2010, with the land use change in the Songnen Plain, the maximum air temperature and diurnal temperature range trend has decreased, the minimum air temperature trend has increased, and impact of land use change on minimum air temperature trend is greater than maximum air temperature trend. (3) The unused land has the highest minimum air temperature, nighttime land surface temperature, and minimum land surface temperature than any other land uses, and all corresponding temperature is lower in the agricultural land and the lowest in the forestland. (4) The conversion from any other land uses to the unused land will result in the increasing of the temperature and to the agricultural land and forest land will cause the decreasing of the temperature.

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January–December 2012. The motivation was to examine the overall performance of data assimilation outputs. The difference statistics of the collocated model outputs versus the radiosonde observations indicated a good agreement for the temperature, amongst datasets, while less agreement was found for the relative humidity. A comparison of the UM outputs from the UKMO and KMA revealed that they are similar to each other. The introduction of the new version of UM into the KMA in May 2012 resulted in an improved analysis performance, particularly for the moisture field. On the other hand, ECMWF reanalysis data showed slightly reduced performance for relative humidity compared with the UM, with a significant humid bias in the upper troposphere. ECMWF reanalysis temperature fields showed nearly the same performance as the two UM analyses. The root mean square differences (RMSDs) of the relative humidity for the three models were larger for more humid conditions, suggesting that humidity forecasts are less reliable under these conditions.

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979–2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP) of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations.

On the Susceptibility of Cold Tropical Cirrus to Ice Nuclei Abundance

Abstract Numerical simulations of cirrus formation in the tropical tropopause layer (TTL) during boreal wintertime are used to evaluate the impact of heterogeneous ice nuclei (IN) abundance on cold cloud microphysical properties and occurrence frequencies. The cirrus model includes homogeneous and heterogeneous ice nucleation, deposition growth/sublimation, and sedimentation. Reanalysis temperature and wind fields with high-frequency waves superimposed are used to force the simulations. The model results are constrained by comparison with in situ and satellite observations of TTL cirrus and relative humidity. Temperature variability driven by high-frequency waves has a dominant influence on TTL cirrus microphysical properties and occurrence frequencies, and inclusion of these waves is required to produce agreement between the simulated and observed abundance of TTL cirrus. With homogeneous freezing only and small-scale gravity waves included in the temperature curtains, the model produces excessive ice concentrations compared with in situ observations. Inclusion of relatively numerous heterogeneous ice nuclei (NIN ≥ 100 L−1) in the simulations improves the agreement with observed ice concentrations. However, when IN contribute significantly to TTL cirrus ice nucleation, the occurrence frequency of large supersaturations with respect to ice is less than indicated by in situ measurements. The model results suggest that the sensitivity of TTL cirrus extinction and ice water content statistics to heterogeneous ice nuclei abundance is relatively weak. The simulated occurrence frequencies of TTL cirrus are quite insensitive to ice nuclei abundance, both in terms of cloud frequency height distribution and regional distribution throughout the tropics.

Predicting the Extent of Wildfires Using Remotely Sensed Soil Moisture and Temperature Trends

Recent climate trends evidence a rise of temperatures and an increase in the duration and intensity of droughts which is in turn leading to the occurrence of larger wildfires, which threaten the environment as well as human lives and beings. In this context, improved wildfires prediction tools are urgently needed. In this paper, the use of remotely sensed soil moisture data as a key variable in the climate-wildfires relationship is explored. The study is centered in the fires registered in the Iberian Peninsula during the period 2010–2014. Their prior-to-occurrence surface moisture-temperature conditions were analyzed using SMOS-derived soil moisture data and ERA-Interim land surface temperature reanalysis. Results showed that moisture and temperature conditions limited the extent of wildfires, and a potential maximum burned area per moisture-temperature paired values was obtained ( R 2 = 0.43). The model relating fire extent with moisture-temperature preconditions was improved by including information on land cover, regions, and the month of the fire outbreak ( R 2 = 0.68). Model predictions had an accuracy of 83.3% with a maximum error of 40.5 ha. Results were majorly coherent with wildfires behavior in the Iberian Peninsula and reflected the duality between Euro-Siberian and Mediterranean regions in terms of expected burned area. The proposed model has a promising potential for the enhancement of fire prevention services.

A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

Summary Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall ( AR ) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the TRMM and TRMM-RT rainfalls at 1–3 days lead times. The results confirm the robustness of the WNARX model with only the satellite-based (TRMM-RT) rainfall (without use of gauge data) to provide reasonably good real-time flood forecasts. The utility of the TRMM-RT solves the real-time flood forecasting issues, since this is the only rainfall product disseminated in real-time. Hence, the WNARX model with the TMPA rainfall products can offer an exciting new horizon to provide flood forecasting and early warning in the flood prone catchments.

When will unusual heat waves become normal in a warming Africa?

Africa is one of the most vulnerable continents to climate change. In the upcoming decades the occurrence of longer, hotter and more frequent heat waves could have a strong impact on human mortality and crop production. Here, by applying the heat wave magnitude index daily to temperature reanalysis data, we quantify the magnitude and the spatial extent of the most extreme heat waves experienced in Africa between 1979 and October 2015 across different seasons. Results show that in the recent years Africa experienced hotter, longer and more extent heat waves than in the last two decades of the 20th century. In the future, 50% of regional climateprojections suggest that heat waves that are unusual under present climate conditions will occur on a regular basis by 2040 under the most severe IPCC AR5 scenario (i.e. RCP8.5).

Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands

Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth’s shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs.

Teleconnection Paths via Climate Network Direct Link Detection.

Teleconnections describe remote connections (typically thousands of kilometers) of the climate system. These are of great importance in climate dynamics as they reflect the transportation of energy and climate change on global scales (like the El Niño phenomenon). Yet, the path of influence propagation between such remote regions, and weighting associated with different paths, are only partially known. Here we propose a systematic climate network approach to find and quantify the optimal paths between remotely distant interacting locations. Specifically, we separate the correlations between two grid points into direct and indirect components, where the optimal path is found based on a minimal total cost function of the direct links. We demonstrate our method using near surface air temperature reanalysis data, on identifying cross-latitude teleconnections and their corresponding optimal paths. The proposed method may be used to quantify and improve our understanding regarding the emergence of climate patterns on global scales.

Intercomparison of the temperature contrast between the arctic and equator in the pre- and post periods of the 1976/1977 regime shift

Based on the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis temperature dataset in the period of 1948–2014, the temperature contrast between the Arctic and equator in the pre- and post periods of the 1976/1977 regime shift is compared. An index measuring the temperature contrast is defined as the difference between the Arctic zone (70° N–90° N) and the equatorial region (10° S–10° N). The variations of the temperature contrast can be mainly explained by the local sea ice variations through sea ice–albedo–temperature feedback before 1976/1977 and the energy transportation to the Arctic together with the local sea ice after 1976/1977. The impacts of the Arctic minus equator (AmE) temperature contrast on the high-level westerly jet, and the polar easterlies show a significant difference during the two periods. A strong temperature anomaly associated with the temperature contrast in the two periods is found in the high latitude, but different patterns are observed at the high and low levels. The correlated water vapor appeared in the Indian Ocean and Maritime Continent before 1976/1977 and moved to northeastern Canada and eastern North America after 1976/1977.

Dynamical, convective, and microphysical control on wintertime distributions of water vapor and clouds in the tropical tropopause layer

AbstractProcesses that influence the humidity and cirrus cloud abundance in the Tropical Tropopause Layer (TTL) during boreal winter 2006–2007 are investigated in simulations of clouds along backward trajectories of parcels ending at the 372 K potential temperature (100 hPa) level in the tropics. Trajectories are calculated using offline calculations of seasonal mean tropical radiative heating rates along with reanalysis temperature and wind data with enhanced wave‐driven variability in the TTL. The one‐dimensional (vertical) time‐dependent cloud microphysical model is initialized with water vapor measurements from the Microwave Limb Sounder and the evolution of clouds along each trajectory is simulated using temperature profiles extracted from reanalysis data and convective cloud top heights estimated from 3‐hourly geostationary satellite imagery. Averaged over the tropics, waves dehydrate the 100 hPa level by 0.5 ppmv, while convection and cloud microphysical processes moisten by 0.3 and 0.7 ppmv, respectively. The tropical mean cloud occurrence frequencies in the middle to upper TTL agree well with those based on satellite observations (spatial correlation of 0.8). Waves and convection enhance cloud occurrence at the cold point tropopause by 4% and 2%, respectively. Temporal variability of the heating rates as indicated by the ERA‐Interim 6‐hourly heating rate fields dehydrates the TTL by 0.4 ppmv and decreases the cloud occurrence by 4% because parcels are more likely to encounter the coldest temperatures and dehydrate near the cold point, limiting cloud formation above. The final dehydration locations of parcels, concentrated near the dateline in the tropical Pacific, are insensitive to various model parameters.

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 2: Case studies and trends of sea ice albedo and melt ponds in the Arctic for years 2002–2011

Abstract. The spatial and temporal dynamics of melt ponds and sea ice albedo contain information on the current state and the trend of the climate of the Arctic region. This publication presents a study on melt pond fraction (MPF) and sea ice albedo spatial and temporal dynamics obtained with the Melt Pond Detection (MPD) retrieval scheme for the Medium Resolution Imaging Spectrometer (MERIS) satellite data. This study compares sea ice albedo and MPF to surface air temperature reanalysis data, compares MPF retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), and examines albedo and MPF trends. Weekly averages of MPF for 2007 and 2011 showed different MPF dynamics while summer sea ice minimum was similar for both years. The gridded MPF and albedo products compare well to independent reanalysis temperature data and show melt onset when the temperature gets above zero; however MPD shows an offset at low MPFs of about 10 % most probably due to unscreened high clouds. Weekly averaged trends show pronounced dynamics of both, MPF and albedo: a negative MPF trend in the East Siberian Sea and a positive MPF trend around the Queen Elizabeth Islands. The negative MPF trend appears due to a change of the absolute MPF value in its peak, whereas the positive MPF trend is created by the earlier melt onset, with the peak MPF values unchanged. The MPF dynamics in the East Siberian Sea could indicate a temporal change of ice type prevailing in the region, as opposed to the Queen Elizabeth Islands, where MPF dynamics react to an earlier seasonal onset of melt.

Seasonal sea surface temperature anomaly prediction for coastal ecosystems

Sea surface temperature (SST) anomalies are often both leading indicators and important drivers of marine resource fluctuations. Assessment of the skill of SST anomaly forecasts within coastal ecosystems accounting for the majority of global fish yields, however, has been minimal. This reflects coarse global forecast system resolution and past emphasis on the predictability of ocean basin-scale SST variations. This paper assesses monthly to inter-annual SST anomaly predictions in coastal “Large Marine Ecosystems” (LMEs). We begin with an analysis of 7 well-observed LMEs adjacent to the United States and then examine how mechanisms responsible for prediction skill in these systems are reflected in predictions for LMEs globally. Historical SST anomaly estimates from the 1/4° daily Optimal Interpolation Sea Surface Temperature reanalysis (OISST.v2) were first found to be highly consistent with in-situ measurements for 6 of the 7 U.S. LMEs. Thirty years of retrospective forecasts from climate forecast systems developed at NOAA’s Geophysical Fluid Dynamics Laboratory (CM2.5-FLOR) and the National Center for Environmental Prediction (CFSv2) were then assessed against OISST.v2. Forecast skill varied widely by LME, initialization month, and lead but there were many cases of high skill that also exceeded that of a persistence forecast, some at leads greater than 6months. Mechanisms underlying skill above persistence included accurate simulation of (a) seasonal transitions between less predictable locally generated and more predictable basin-scale SST variability; (b) seasonal transitions between different basin-scale influences; (c) propagation of SST anomalies across seasons through sea ice; and (d) re-emergence of previous anomalies upon the breakdown of summer stratification. Globally, significant skill above persistence across many tropical systems arises via mechanisms (a) and (b). Combinations of all four mechanisms contribute to less prevalent but nonetheless significant skill in extratropical systems. While continued refinement of global climate forecast systems and observations are needed to improve coastal SST anomaly prediction and extend predictions to other ecosystem relevant variables (e.g., salinity), present skill warrants close examination of forecasts for marine resource applications.

Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies

Abstract. This study validates the near-surface temperature and precipitation output from decadal runs of eight atmospheric ocean general circulation models (AOGCMs) against observational proxy data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis temperatures and Global Precipitation Climatology Project (GPCP) precipitation data. We model the joint distribution of these two fields with a parsimonious bivariate Matérn spatial covariance model, accounting for the two fields' spatial cross-correlation as well as their own smoothnesses. We fit output from each AOGCM (30-year seasonal averages from 1981 to 2010) to a statistical model on each of 21 land regions. Both variance and smoothness values agree for both fields over all latitude bands except southern mid-latitudes. Our results imply that temperature fields have smaller smoothness coefficients than precipitation fields, while both have decreasing smoothness coefficients with increasing latitude. Models predict fields with smaller smoothness coefficients than observational proxy data for the tropics. The estimated spatial cross-correlations of these two fields, however, are quite different for most GCMs in mid-latitudes. Model correlation estimates agree well with those for observational proxy data for Australia, at high northern latitudes across North America, Europe and Asia, as well as across the Sahara, India, and Southeast Asia, but elsewhere, little consistent agreement exists.

Land surface temperature retrieval over circumpolar Arctic using SSM/I–SSMIS and MODIS data

Remote sensing instruments are key players to map land surface temperature (LST) at large temporal and spatial scales. In this paper, we present how we combine passive microwave and thermal infrared data to estimate LST during summer snow-free periods over northern high latitudes. The methodology is based on the SSM/I–SSMIS 37GHz measurements at both vertical and horizontal polarizations on a 25km×25km grid size. LST is retrieved from brightness temperatures introducing an empirical linear relationship between emissivities at both polarizations as described in Royer and Poirier (2010). This relationship is calibrated at pixel scale, using cloud-free independent LST data from MODIS instruments. The SSM/I–SSMIS and MODIS data are synchronized by fitting a diurnal cycle model built on skin temperature reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The resulting temperature dataset is provided at 25km scale and at an hourly time step during the ten-year analysis period (2000–2011). This new product was locally evaluated at five experimental sites of the EU-PAGE21 project against air temperature measurements and meteorological model reanalysis, and compared to the MODIS LST product at both local and circumpolar scale. The results giving a mean RMSE of the order of 2.2K demonstrate the usefulness of the microwave product, which is unaffected by clouds as opposed to thermal infrared products and offers a better resolution compared to model reanalysis. The dataset can be downloaded from the PANGAEA website: http://doi.pangaea.de/10.1594/PANGAEA.833409.