Surface properties, such as roughness and vegetation, which vary both within and between urban areas, play a dominant role in determining surface-atmosphere energy exchanges. The turbulent heat flux partitioning is examined within a single urban area through measurements at four locations in Łódź, Poland, during August 2002. The dominant surface cover (land use) at the sites was grass (airport), 1-3-story detached houses with trees (residential), large 2-4-story buildings (industrial), and 3-6-story buildings (downtown). However, vegetation, buildings, and other "impervious" surface coverage vary within some of these sites on the scale of the turbulent flux measurements. Vegetation and building cover for Łódź were determined from remotely sensed data and an existing database. A source-area model was then used to develop a lookup table to estimate surface cover fractions more accurately for individual measurements. Bowen ratios show an inverse relation with increasing vegetation cover both for a site and, more significant, between sites, as expected. Latent heat fluxes at the residential site were less dependent on short-term rainfall than at the grass site. Sensible heat fluxes were positively correlated with impervious surface cover and building intensity. These results are consistent with previous findings (focused mainly on differences between cities) and highlight the value of simple measures of land cover as predictors of spatial variations of urban climates both within and between urban areas.

Abstract. Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO 2 fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5 • grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V cmax ) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r 2 = 0.76; NashSutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r 2 = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r 2 = 0.42, MEF = 0.14) and and net ecosystem CO 2 exchange (NEE, r 2 = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r 2 values (0.57-0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r 2 values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r 2 < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V cmax value.

This can be attributed to anthropogenic CO 2 emissions, which are particularly strong in winter due to, among other things, mineral fuel combustion during domestic heating. The average monthly fluxes are positive in all seasons, which means that emission of CO 2 in the surroundings of the measurement point prevails over its uptake. Apart from the season, the maximum flux occurred during the day and the minimum during the second part of the night. Wintertime monthly averaged fluxes are much higher than summertime ones. The observed increase in CO 2 exchange during weekdays in comparison with weekends can be caused by the weekly rhythm of traffic in the surroundings of Lipowa Station.

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Abstract. Peatlands store substantial amount of carbon, are vulnerable to climate change. To predict the fate of carbon stored in peatlands, the complex interactions between water, peat and vegetations need more attention. This study describes a modified version of the ORCHIDEE land surface model for simulating the hydrology, surface energy and CO2 fluxes of peatlands on daily to annual time scales. The model, referred to as ORCHIDEE-PEAT, includes a separate soil tile in each 0.5° grid-cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation with a grid-cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model is evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (Vcmax) being optimized at each site to match the peak of growing season gross primary productivity (GPP), derived from direct EC measurements. Regarding short-term variations from day to day, the model performance was good for the variations in GPP (r2 = 0.76, Nash-Sutcliff modeling efficiency, MEF = 0.76), with lesser accuracy for latent heat fluxes (LE, r2 = 0.42, MEF = 0.14) and Net ecosystem CO2 exchange (NEE, r2 = 0.38, MEF = 0.26). Seasonal variations in GPP, NEE and energy fluxes on monthly scales showed moderate to high r2 values ranging from 0.57 to 0.86. For spatial across-sites gradients of annual mean GPP, NEE and LE, r2 of 0.93, 0.27, and 0.71, respectively, were achieved. The water table variations are not well predicted (r2 < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, when using the observed water table in the carbon module to define the fraction of oxic and anoxic decomposition instead of the modeled water table, ORCHIDEE-PEAT shows a small improvement in reproducing NEE. Moreover, we found a significant relationship between optimized Vcmax and the latitude (temperature), which can better reflect the spatial gradients of annual NEE than using an average Vcmax value. In a future version of ORCHIDEE-PEAT, the influences of water table on photosynthesis and depth-dependent influences of soil temperature on respiration may be included.