A high-resolution near-infrared extraterrestrial solar spectrum derived from ground-based Fourier transform spectrometer measurements

A detailed spectrally-resolved extraterrestrial solar spectrum (ESS) is important for line-by-line radiative transfer modeling in the near-infrared (near-IR). Very few observationally-based high-resolution ESS are available in this spectral region. Consequently the theoretically-calculated ESS by Kurucz has been widely adopted. We present the CAVIAR (Continuum Absorption at Visible and Infrared Wavelengths and its Atmospheric Relevance) ESS which is derived using the Langley technique applied to calibrated observations using a ground-based high-resolution Fourier transform spectrometer (FTS) in atmospheric windows from 2000–10000 cm-1 (1–5 μm). There is good agreement between the strengths and positions of solar lines between the CAVIAR and the satellite-based ACE-FTS (Atmospheric Chemistry Experiment-FTS) ESS, in the spectral region where they overlap, and good agreement with other ground-based FTS measurements in two near-IR windows. However there are significant differences in the structure between the CAVIAR ESS and spectra from semi-empirical models. In addition, we found a difference of up to 8 % in the absolute (and hence the wavelength-integrated) irradiance between the CAVIAR ESS and that of Thuillier et al., which was based on measurements from the Atmospheric Laboratory for Applications and Science satellite and other sources. In many spectral regions, this difference is significant, as the coverage factor k = 2 (or 95 % confidence limit) uncertainties in the two sets of observations do not overlap. Since the total solar irradiance is relatively well constrained, if the CAVIAR ESS is correct, then this would indicate an integrated “loss” of solar irradiance of about 30 W m-2 in the near-IR that would have to be compensated by an increase at other wavelengths.

High resolution simulation of recent Arctic and Antarctic stratospheric chemical ozone loss compared to observations

Simulations of polar ozone losses were performed using the three-dimensional high-resolution (1∘ × 1∘) chemical transport model MIMOSA-CHIM. Three Arctic winters 1999–2000, 2001–2002, 2002–2003 and three Antarctic winters 2001, 2002, and 2003 were considered for the study. The cumulative ozone loss in the Arctic winter 2002–2003 reached around 35% at 475 K inside the vortex, as compared to more than 60% in 1999–2000. During 1999–2000, denitrification induces a maximum of about 23% extra ozone loss at 475 K as compared to 17% in 2002–2003. Unlike these two colder Arctic winters, the 2001–2002 Arctic was warmer and did not experience much ozone loss. Sensitivity tests showed that the chosen resolution of 1∘ × 1∘ provides a better evaluation of ozone loss at the edge of the polar vortex in high solar zenith angle conditions. The simulation results for ozone, ClO, HNO3, N2O, and NO y for winters 1999–2000 and 2002–2003 were compared with measurements on board ER-2 and Geophysica aircraft respectively. Sensitivity tests showed that increasing heating rates calculated by the model by 50% and doubling the PSC (Polar Stratospheric Clouds) particle density (from 5 × 10−3 to 10−2 cm−3) refines the agreement with in situ ozone, N2O and NO y levels. In this configuration, simulated ClO levels are increased and are in better agreement with observations in January but are overestimated by about 20% in March. The use of the Burkholder et al. (1990) Cl2O2 absorption cross-sections slightly increases further ClO levels especially in high solar zenith angle conditions. Comparisons of the modelled ozone values with ozonesonde measurement in the Antarctic winter 2003 and with Polar Ozone and Aerosol Measurement III (POAM III) measurements in the Antarctic winters 2001 and 2002, shows that the simulations underestimate the ozone loss rate at the end of the ozone destruction period. A slightly better agreement is obtained with the use of Burkholder et al. (1990) Cl2O2 absorption cross-sections.

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or ‘truth’ trajectory, as well as with analyses using the ‘true’ observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm.

Very high resolution bioclimatic zoning of Portuguese wine regions: present and future scenarios

Wine production is strongly affected by weather and climate and thus highly vulnerable to climate change. In Portugal, viticulture and wine production are an important economic activity. In the present study, current bioclimatic zoning in Portugal (1950–2000) and its projected changes under future climate conditions (2041–2070) are assessed through the analysis of an aggregated, categorized bioclimatic index (CatI) at a very high spatial resolution (near 1 km). CatI incorporates the most relevant bioclimatic characteristics of a given region, thus allowing the direct comparison between different regions. Future viticultural zoning is achieved using data from 13 climate model transient experiments following the A1B emission scenario. These data are downscaled using a two-step method of spatial pattern downscaling. This downscaling approach allows characterizing mesoclimatic influences on viticulture throughout Portugal. Results for the recent past depict the current spatial variability of Portuguese viticultural regions. Under future climate conditions, the current viticultural zoning is projected to undergo significant changes, which may represent important challenges for the Portuguese winemaking sector. The changes are quite robust across the different climate models. A lower bioclimatic diversity is also projected, resulting from a more homogeneous warm and dry climate in most of the wine regions. This will lead to changes in varietal suitability and wine characteristics of each region.

Statistical–dynamical downscaling of present day and future precipitation regimes in the Aksu river catchment in Central Asia

A statistical–dynamical downscaling (SDD) approach is applied to determine present day and future high-resolution rainfall distributions in the catchment of the river Aksu at the southern slopes of the Tienshan Mountains, Central Asia. First, a circulation weather type (CWT) classification is employed to define typical lower atmospheric flow regimes from ERA-40 reanalysis data. Selected representatives of each CWT are dynamically downscaled with the regional climate model COSMO-CLM 4.8 at a horizontal grid resolution of 0.0625°, using the ERA-40 reanalysis data as boundary conditions. Finally, the simulated representatives are recombined to obtain a high-resolution rainfall climatology for present day climate. The methodology is also applied to ensemble simulations of three different scenarios of the global climate model ECHAM5/MPI-OM1 to derive projections of rainfall changes until 2100. Comparisons of downscaled seasonal and annual rainfall with observational data suggest that the statistical–dynamical approach is appropriate to capture the observed present-day precipitation climatology over the low lands and the first elevations of the Tienshan Mountains. On the other hand, a strong bias is found at higher altitudes, where precipitation is clearly underestimated by SDD. The application of SDD to the ECHAM5/MPI-OM1 ensemble reveals that precipitation changes by the end of the 21st century depend on the season. While for autumn an increase of seasonal precipitation is found for all simulations, a decrease in precipitation is obtained during winter for most parts of the Aksu catchment. The spread between different ECHAM5/MPI-OM1 ensemble members is strongest in spring, where trends of opposite sign are found. The largest changes in rainfall are simulated for the summer season, which also shows the most pronounced spatial heterogeneity. Most ECHAM5/MPI-OM1 realizations indicate a decrease of annual precipitation over large parts of the Tienshan, and an increase restricted to the southeast of the study area. These results provide a good basis for downscaling present-day and future rainfall distributions for hydrological purposes.

Heat and moisture budgets from airborne measurements and high-resolution model simulations

High-resolution simulations with a mesoscale model are performed to estimate heat and moisture budgets of a well-mixed boundary layer. The model budgets are validated against energy budgets obtained from airborne measurements over heterogeneous terrain in Western Germany. Time rate of change, vertical divergence, and horizontal advection for an atmospheric column of air are estimated. Results show that the time trend of specific humidity exhibits some deficiencies, while the potential temperature trend is matched accurately. Furthermore, the simulated turbulent surface fluxes of sensible and latent heat are comparable to the measured fluxes, leading to similar values of the vertical divergence. The analysis of different horizontal model resolutions exhibits improved surface fluxes with increased resolution, a fact attributed to a reduced aggregation effect. Scale-interaction effects could be identified: while time trends and advection are strongly influenced by mesoscale forcing, the turbulent surface fluxes are mainly controlled by microscale processes.

Sensitivities of a cyclone detection and tracking algorithm: individual tracks and climatology

Northern Hemisphere cyclone activity is assessed by applying an algorithm for the detection and tracking of synoptic scale cyclones to mean sea level pressure data. The method, originally developed for the Southern Hemisphere, is adapted for application in the Northern Hemisphere winter season. NCEP-Reanalysis data from 1958/59 to 1997/98 are used as input. The sensitivities of the results to particular parameters of the algorithm are discussed for both case studies and from a climatological point of view. Results show that the choice of settings is of major relevance especially for the tracking of smaller scale and fast moving systems. With an appropriate setting the algorithm is capable of automatically tracking different types of cyclones at the same time: Both fast moving and developing systems over the large ocean basins and smaller scale cyclones over the Mediterranean basin can be assessed. The climatology of cyclone variables, e.g., cyclone track density, cyclone counts, intensification rates, propagation speeds and areas of cyclogenesis and -lysis gives detailed information on typical cyclone life cycles for different regions. The lowering of the spatial and temporal resolution of the input data from full resolution T62/06h to T42/12h decreases the cyclone track density and cyclone counts. Reducing the temporal resolution alone contributes to a decline in the number of fast moving systems, which is relevant for the cyclone track density. Lowering spatial resolution alone mainly reduces the number of weak cyclones.

Differentiation of neotropical ecosystems by modern soil phytolith assemblages and its implications for palaeoenvironmental and archaeological reconstructions

The interpretation of Neotropical fossil phytolith assemblages for palaeoenvironmental and archaeological reconstructions relies on the development of appropriate modern analogues. We analyzed modern phytolith assemblages from the soils of ten distinctive tropical vegetation communities in eastern lowland Bolivia, ranging from terra firme humid evergreen forest to seasonally-inundated savannah. Results show that broad ecosystems – evergreen tropical forest, semi-deciduous dry tropical forest, and savannah – can be clearly differentiated by examination of their phytolith spectra and the application of Principal Component Analysis (PCA). Differences in phytolith assemblages between particular vegetation communities within each of these ecosystems are more subtle, but can still be identified. Comparison of phytolith assemblages with pollen rain data and stable carbon isotope analyses from the same vegetation plots show that these proxies are not only complementary, but significantly improve taxonomic and ecosystem resolution, and therefore our ability to interpret palaeoenvironmental and archaeological records. Our data underline the utility of phytolith analyses for reconstructing Amazon Holocene vegetation histories and pre-Columbian land use, particularly the high spatial resolution possible with terrestrial soil-based phytolith studies.

Effects of spatial resolution in the simulation of daily and subdaily precipitation in the southwestern US

We evaluate the effects of spatial resolution on the ability of a regional climate model to reproduce observed extreme precipitation for a region in the Southwestern United States. A total of 73 National Climate Data Center observational sites spread throughout Arizona and New Mexico are compared with regional climate simulations at the spatial resolutions of 50 km and 10 km for a 31 year period from 1980 to 2010. We analyze mean, 3-hourly and 24-hourly extreme precipitation events using WRF regional model simulations driven by NCEP-2 reanalysis. The mean climatological spatial structure of precipitation in the Southwest is well represented by the 10 km resolution but missing in the coarse (50 km resolution) simulation. However, the fine grid has a larger positive bias in mean summer precipitation than the coarse-resolution grid. The large overestimation in the simulation is in part due to scale-dependent deficiencies in the Kain-Fritsch convective parameterization scheme that generate excessive precipitation and induce a slow eastward propagation of the moist convective summer systems in the high-resolution simulation. Despite this overestimation in the mean, the 10 km simulation captures individual extreme summer precipitation events better than the 50 km simulation. In winter, however, the two simulations appear to perform equally in simulating extremes.

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.

The GODAE high resolution sea surface temperature pilot project

Sea surface temperature (SST) measurements are required by operational ocean and atmospheric forecasting systems to constrain modeled upper ocean circulation and thermal structure. The Global Ocean Data Assimilation Experiment (GODAE) High Resolution SST Pilot Project (GHRSST-PP) was initiated to address these needs by coordinating the provision of accurate, high-resolution, SST products for the global domain. The pilot project is now complete, but activities continue within the Group for High Resolution SST (GHRSST). The pilot project focused on harmonizing diverse satellite and in situ data streams that were indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework implemented in an internationally distributed manner. Data with meaningful error estimates developed within GHRSST are provided by services within R/GTS. Currently, several terabytes of data are processed at international centers daily, creating more than 25 gigabytes of product. Ensemble SST analyses together with anomaly SST outputs are generated each day, providing confidence in SST analyses via diagnostic outputs. Diagnostic data sets are generated and Web interfaces are provided to monitor the quality of observation and analysis products. GHRSST research and development projects continue to tackle problems of instrument calibration, algorithm development, diurnal variability, skin temperature deviation, and validation/verification of GHRSST products. GHRSST also works closely with applications and users, providing a forum for discussion and feedback between SST users and producers on a regular basis. All data within the GHRSST R/GTS framework are freely available. This paper reviews the progress of GHRSST-PP, highlighting achievements that have been fundamental to the success of the pilot project.

Impact of city changes and weather on anthropogenic heat flux in Europe 1995–2015

How people live, work, move from place to place, consume and the technologies they use all affect heat emissions in a city which influences urban weather and climate. Here we document changes to a global anthropogenic heat flux (QF) model to enhance its spatial (30′′ × 30′′ to 0.5° × 0.5°) resolution and temporal coverage (historical, current and future). QF is estimated across Europe (1995–2015), considering changes in temperature, population and energy use. While on average QF is small (of the order 1.9–4.6 W m−2 across all the urban areas of Europe), significant spatial variability is documented (maximum 185 W m−2). Changes in energy consumption due to changes in climate are predicted to cause a 13% (11%) increase in QF on summer (winter) weekdays. The largest impact results from changes in temperature conditions which influences building energy use; for winter, with the coldest February on record, the mean flux for urban areas of Europe is 4.56 W m−2 and for summer (warmest July on record) is 2.23 W m−2. Detailed results from London highlight the spatial resolution used to model the QF is critical and must be appropriate for the application at hand, whether scientific understanding or decision making.