Idealized model for changes in equilibrium temperature, mixed layer depth, and boundary layer cloud over land in a doubled CO2 climate

An idealized equilibrium model for the undisturbed partly cloudy boundary layer (BL) is used as a framework to explore the coupling of the energy, water, and carbon cycles over land in midlatitudes and show the sensitivity to the clear‐sky shortwave flux, the midtropospheric temperature, moisture, CO2, and subsidence. The changes in the surface fluxes, the BL equilibrium, and cloud cover are shown for a warmer, doubled CO2 climate. Reduced stomatal conductance in a simple vegetation model amplifies the background 2 K ocean temperature rise to an (unrealistically large) 6 K increase in near‐surface temperature over land, with a corresponding drop of near‐surface relative humidity of about 19%, and a rise of cloud base of about 70 hPa. Cloud changes depend strongly on changes of mean subsidence; but evaporative fraction (EF) decreases. EF is almost uniquely related to mixed layer (ML) depth, independent of background forcing climate. This suggests that it might be possible to infer EF for heterogeneous landscapes from ML depth. The asymmetry of increased evaporation over the oceans and reduced transpiration over land increases in a warmer doubled CO2 climate.

Sensitivity of a coupled climate model to canopy interception capacity

The canopy interception capacity is a small but key part of the surface hydrology, which affects the amount of water intercepted by vegetation and therefore the partitioning of evaporation and transpiration. However, little research with climate models has been done to understand the effects of a range of possible canopy interception capacity parameter values. This is in part due to the assumption that it does not significantly affect climate. Near global evapotranspiration products now make evaluation of canopy interception capacity parameterisations possible. We use a range of canopy water interception capacity values from the literature to investigate the effect on climate within the climate model HadCM3. We find that the global mean temperature is affected by up to -0.64 K globally and -1.9 K regionally. These temperature impacts are predominantly due to changes in the evaporative fraction and top of atmosphere albedo. In the tropics, the variations in evapotranspiration affect precipitation, significantly enhancing rainfall. Comparing the model output to measurements, we find that the default canopy interception capacity parameterisation overestimates canopy interception loss (i.e. canopy evaporation) and underestimates transpiration. Overall, decreasing canopy interception capacity improves the evapotranspiration partitioning in HadCM3, though the measurement literature more strongly supports an increase. The high sensitivity of climate to the parameterisation of canopy interception capacity is partially due to the high number of light rain-days in the climate model that means that interception is overestimated. This work highlights the hitherto underestimated importance of canopy interception capacity in climate model hydroclimatology and the need to acknowledge the role of precipitation representation limitations in determining parameterisations.

Estimating surface CO2 fluxes from space-borne dry air mole fraction observations using an Ensemble Kalman filter

We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2 measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2 measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble. At the revision stage of this manuscript, the OCO instrument failed to reach its orbit after it was launched on 24 February 2009. The EnKF formulation presented here is also applicable to GOSAT measurements of CO2 and CH4.

Fe-sulphate-rich evaporative mineral precipitates from the Rio Tinto, southwest Spain

The soluble metal sulphate salts melanterite, rozenite, rhomboclase, szornolnokite, copiapite, coquimbite, hexahydrite and halotrichite, together with gypsum, have been identified, some for the first time oil the banks of the Rio Tinto, SW Spain. Secondary Fe-sulphate minerals call form directly from evaporating acid, sulphate-rich Solutions as a result of pyrite oxidation. Chemical analyses of mixtures of these salt minerals indicate concentrations of Fe (up to 31 wt.%), Mg (up to 4 wt.%), Cu (up to 2 wt.%) and Zn (up to wt.%). These minerals are shown to act as transient storage Cor metals and can store on average up to 10% (9.5 - 11%) and 22% (20-23%) Zn and Cu respectively, of the total discharge of the Rio Tinto during the summer period. Melanterite and rozenite precipitates at Rio Tinto are only found in association with very acidic drainage waters (pH <1.0) draining directly from pyritic waste piles. Copiapite precipitates abundantly oil the banks of the Rio Tinto by (1) direct evaporation of the river water; or (2) as part of a paragenetic sequence with the inclusion of minor halotrichite, indicating natural dehydration and decomposition. The natural occurrences are comparable with the process of paragenesis from the evaporation of Rio Tinto river water under laboratory experiments resulting in the formation of aluminocopiapite, halotrichite, coquimbite, voltaite and gypsum.

Sampling uncertainty properties of cloud fraction estimates from random transect observations

[1] Cloud cover is conventionally estimated from satellite images as the observed fraction of cloudy pixels. Active instruments such as radar and Lidar observe in narrow transects that sample only a small percentage of the area over which the cloud fraction is estimated. As a consequence, the fraction estimate has an associated sampling uncertainty, which usually remains unspecified. This paper extends a Bayesian method of cloud fraction estimation, which also provides an analytical estimate of the sampling error. This method is applied to test the sensitivity of this error to sampling characteristics, such as the number of observed transects and the variability of the underlying cloud field. The dependence of the uncertainty on these characteristics is investigated using synthetic data simulated to have properties closely resembling observations of the spaceborne Lidar NASA-LITE mission. Results suggest that the variance of the cloud fraction is greatest for medium cloud cover and least when conditions are mostly cloudy or clear. However, there is a bias in the estimation, which is greatest around 25% and 75% cloud cover. The sampling uncertainty is also affected by the mean lengths of clouds and of clear intervals; shorter lengths decrease uncertainty, primarily because there are more cloud observations in a transect of a given length. Uncertainty also falls with increasing number of transects. Therefore a sampling strategy aimed at minimizing the uncertainty in transect derived cloud fraction will have to take into account both the cloud and clear sky length distributions as well as the cloud fraction of the observed field. These conclusions have implications for the design of future satellite missions. This paper describes the first integrated methodology for the analytical assessment of sampling uncertainty in cloud fraction observations from forthcoming spaceborne radar and Lidar missions such as NASA's Calipso and CloudSat.