Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Karydis, Vlassis A.; Capps, Shannon L.; Russell, Armistead G.; Nenes, Athanasios

doi:10.5194/acp-12-9041-2012

Cited by 52 publications

(67 citation statements)

References 88 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the first-order derivatives of FN and ARG introduced in Appendix A has been extensively tested by comparing them against central difference computations (e.g., Karydis et al, 2012b). In this section however, we perform an evaluation of the adjoint sensitivities against detailed numerical simulations of the activation process, since this provides a method for validating the physical consistency of the parameterization-derived sensitivities.…”

Section: Appendix B Validation Of Parameterization Derivativesmentioning

confidence: 99%

“…The development of the adjoint of the Fountoukis and Nenes (2005) parameterization (FN), as well as that for the adsorption activation parameterization of Kumar et al (2009), is described in full detail in Karydis et al (2012b). Briefly, because the computation of N d in FN is achieved by iterative solution of Eq.…”

Section: A1 Fn and Fn-il Parameterizationsmentioning

confidence: 99%

“…The first step is the computation of the CCN spectrum, i.e., the cumulative number of particles with critical supersaturation less than a given value s. The second step consists of determining the maximum supersaturation, s max , that develops in an ascending air parcel that rises with updraft velocity, w, and includes the water vapor condensation sink provided by the CCN computed in the previous step. The first step is achieved by mapping the aerosol size distribution and chemical composition onto supersaturation space (e.g., Fountoukis and Nenes, 2005;Karydis et al, 2012b), i.e.,…”

Section: Adjoint Developmentmentioning

confidence: 99%

“…Therefore, efficient computation of the sensitivities in the FN framework required the development of a corresponding adjoint code. For this, we implemented the newly developed adjoint sensitivity of the FN and FN-IL (Karydis et al, 2012b), which uses automatic differentiation software to build the necessary subroutines.…”

Section: Adjoint Sensitivities Of N D To Aerosol Propertiesmentioning

confidence: 99%

“…(A3), the computation of the sensitivities has to be achieved by performing a line-by-line differentiation of the numerical routines. Karydis et al (2012b) used the automatic differentiation software TAPENADE to construct the routines necessary for efficient computation of derivatives. The FN-adjoint built with this procedure yields the set of sensitivities of N d with analytical precision, and the computational cost of the computation is a constant multiple, independent of the number of input parameters, of the cost of computing N d .…”

Section: A1 Fn and Fn-il Parameterizationsmentioning

confidence: 99%

See 4 more Smart Citations

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Betancourt

Nenes

2014

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. Aerosol indirect effects in climate models strongly depend on the representation of the aerosol activation process. In this study, we assess the process-level differences across activation parameterizations that contribute to droplet number uncertainty by using the adjoints of the AbdulRazzak and Ghan (2000) and Fountoukis and Nenes (2005) droplet activation parameterizations in the framework of the Community Atmospheric Model version 5.1 (CAM5.1). The adjoint sensitivities of N d to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes in N d between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences in N d fields and aerosol indirect effect estimates when different activation schemes are used within the same modeling framework. The sensitivities are computed online at minimal computational cost. Changes in aerosol number and aerosol mass concentrations were found to contribute to N d differences much more strongly than chemical composition effects. The main sources of discrepancy between the activation parameterizations considered were the treatment of the water uptake by coarse mode particles, and the sensitivity of the parameterized N d accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions of N d over land and over oceans when these parameterizations are employed. Discrepancies in the sensitivity to aerosol size are responsible for an exaggerated response to aerosol volume changes over heavily polluted regions. Because these regions are collocated with areas of deep clouds, their impact on shortwave cloud forcing is amplified through liquid water path changes. The same framework is also utilized to efficiently explore droplet number uncertainty attributable to hygroscopicity parameter of organic aerosol (primary and secondary). Comparisons between the parameterization-derived sensitivities of droplet number against predictions with detailed numerical simulations of the activation process were performed to validate the physical consistency of the adjoint sensitivities.

show abstract

Section: Appendix B Validation Of Parameterization Derivativesmentioning

confidence: 99%

Section: A1 Fn and Fn-il Parameterizationsmentioning

confidence: 99%

Section: Adjoint Developmentmentioning

confidence: 99%

Section: Adjoint Sensitivities Of N D To Aerosol Propertiesmentioning

confidence: 99%

Section: A1 Fn and Fn-il Parameterizationsmentioning

confidence: 99%

See 3 more Smart Citations

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Betancourt

Nenes

2014

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

Quantifying sensitivities of ice crystal number and sources of ice crystal number variability in CAM 5.1 using the adjoint of a physically based cirrus formation parameterization

et al. 2015

Self Cite

View full text Add to dashboard Cite

(2015), Quantifying sensitivities of ice crystal number and sources of ice crystal number variability in CAM 5.1 using the adjoint of a physically based cirrus formation parameterization, J. Geophys. Res. Atmos., 120, 2834-2854, doi:10.1002 AbstractWe present the adjoint of a cirrus formation parameterization that computes the sensitivity of ice crystal number concentration to updraft velocity, aerosol, and ice deposition coefficient. The adjoint is driven by simulations from the National Center for Atmospheric Research Community Atmosphere Model version 5.1 CAM 5.1 to understand the sensitivity of formed ice crystal number concentration to 13 variables and quantify which contribute to its variability. Sensitivities of formed ice crystal number concentration to updraft velocity, sulfate number, and is sufficient but sulfate number concentration is low, indicating a sulfate-limited regime. Outside of the tropics, competition between homogeneous and heterogeneous nucleation may shift annually averaged sensitivities to higher magnitudes, when infrequent strong updrafts shift crystal production away from purely heterogeneous nucleation. Outside the tropics, updraft velocity is responsible for approximately 52.70% of the ice crystal number variability. In the tropics, sulfate number concentration and updraft jointly control variability in formed crystal number concentration. Insoluble aerosol species play a secondary, but still important, role in influencing the variability in crystal concentrations, with coarse-mode dust being the largest contributor at nearly 50% in certain regions. On a global scale, more than 95% of the temporal variability in crystal number concentration can be described by temperature, updraft velocity, sulfate number, and coarse-mode dust number concentration.

show abstract

Primary marine aerosol‐cloud interactions off the coast of California

et al. 2015

Self Cite

View full text Add to dashboard Cite

Primary marine aerosol (PMA)-cloud interactions off the coast of California were investigated using observations of marine aerosol, cloud condensation nuclei (CCN), and stratocumulus clouds during the Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE) and the Stratocumulus Observations of Los-Angeles Emissions Derived Aerosol-Droplets (SOLEDAD) studies. Based on recently reported measurements of PMA size distributions, a constrained lognormal-mode-fitting procedure was devised to isolate PMA number size distributions from total aerosol size distributions and applied to E-PEACE measurements. During the 12 day E-PEACE cruise on the R/V Point Sur, PMA typically contributed less than 15% of total particle concentrations. PMA number concentrations averaged 12 cm À3 during a relatively calmer period (average wind speed 12 m/s 1 ) lasting 8 days, and 71 cm À3 during a period of higher wind speeds (average 16 m/s 1 ) lasting 5 days. On average, PMA contributed less than 10% of total CCN at supersaturations up to 0.9% during the calmer period; however, during the higher wind speed period, PMA comprised 5-63% of CCN (average 16-28%) at supersaturations less than 0.3%. Sea salt was measured directly in the dried residuals of cloud droplets during the SOLEDAD study. The mass fractions of sea salt in the residuals averaged 12 to 24% during three cloud events. Comparing the marine stratocumulus clouds sampled in the two campaigns, measured peak supersaturations were 0.2 ± 0.04% during E-PEACE and 0.05-0.1% during SOLEDAD. The available measurements show that cloud droplet number concentrations increased with >100 nm particles in E-PEACE but decreased in the three SOLEDAD cloud events.

show abstract

Adjoint sensitivity of global cloud droplet number to aerosol and dynamical parameters

Cited by 52 publications

References 88 publications

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model

Quantifying sensitivities of ice crystal number and sources of ice crystal number variability in CAM 5.1 using the adjoint of a physically based cirrus formation parameterization

Primary marine aerosol‐cloud interactions off the coast of California

Contact Info

Product

Resources

About